DEV Community: Irina Kozlova

Security Scanning in Software Testing Explained: Tools, Process & Advantages

Irina Kozlova — Thu, 02 Jul 2026 17:34:59 +0000

In 2026, we see that security threats remain one of the most pressing concerns for software engineering teams worldwide. A 2025 report shows a 34% increase in attackers compared to last year, with vulnerabilities being exploited to gain access and cause security breaches.

This evolution toward agent-based security scanning allows tools to operate within real execution flows rather than outside them, with modern platforms increasingly leveraging an AI DevSecOps Agent to identify and respond to vulnerabilities in real time.

Even with advances in secure infrastructure, automation, and DevSecOps practices, apps continue to add new attack surfaces with every release.

Security risks can arise from APIs, dependencies, configurations, and continuously changing development and testing environments. This is why integrating security scanning and automated vulnerability scanning right from the design phase is essential.

In this blog, we will look at what security scanning is, why it matters in modern SDLC, and how it can help you stay ahead of emerging threats.

What Is Security Scanning and Why Is It Critical In the SDLC?

Security scanning is the process of analyzing your app’s code, configurations, and components to uncover potential vulnerabilities such as broken authentication or access controls, insecure dependencies, or unencrypted data that threat attackers can exploit.

The main goal of security scanning is to detect these risks early in the development cycle so you can fix them before your app reaches users. Skipping or even delaying your security scans can create major downstream problems and may lead to compliance risks.

Modern teams rely on automated vulnerability scanning to continuously analyze code, dependencies, and configurations without slowing down development.

These are some of the precise reasons why it’s important to integrate security scanning as early as possible in your SDLC:

Higher cost of later-stage remediation: When you find vulnerabilities closer to or after production, they can be way more expensive to fix than those found during the development phase. It can also increase technical complexity and delay releases.
Risk of compliance failures: Regulatory standards like PCI DSS, SOC 2, and GDPR require vulnerability management to be a mandatory part of the development process. And noncompliance can result in failed audits and legal or financial penalties.
Slower delivery speed: Vulnerabilities like insecure APIs or injection flaws, when found late in the SDLC, can force you into rework and disrupt planned sprint cycles.
Lack of visibility into app posture: Inconsistent scans can make it hard for your team to understand which components are at risk the most and how to prioritize fixes, ultimately leading to security gaps.

Why test environments are the new security frontier

Test environments are an essential part of your SDLC, and they can also pose serious threats from attackers.

This is because:

Test data often looks like real customer data
Your app under test has live connections to databases, APIs, and services
End-to-end user journeys mirror how your app will actually be used
Now, most test environment security scanners focus mainly on code or configuration. But they might overlook what happens during the test execution.

Your test data may accidentally contain sensitive or production-like information. You may be using third-party tools and services for testing that can also introduce potential vulnerabilities. Or your test environment grants permissions to users, roles, and systems more than intended.

Therefore, to ensure continuous and robust security scanning, it’s essential to select an effective scanning tool that protects your test data, environment, and application.

Key Features of Modern Security Scanning Tool

These are some of the key characteristics you must consider in a scanning tool:

1. Automated scheduling and scanning

Automated vulnerability scanning and scheduling help you run regular and repeatable security checks without manual intervention, so you can ensure continuous visibility into issues and configuration drifts.

Plus, automated scans support asset discovery and reporting to security or compliance teams, which in turn, improves vulnerability management.

2. Easy, no-frills integration

The security scanning tool must be able to integrate with source control systems, CI/CD pipelines, issue trackers, and cloud platforms. This helps you easily embed continuous vulnerability scanning directly into your development and testing workflows rather than treating the process as a separate activity.

3. Low false positive rate

Tools that have advanced detection algorithms and regular database updates, or use correlation techniques and contextual analysis, reduce the chance of misidentifying security flaws and generating a large amount of false positives. This allows your team to focus on resolving genuine risks instead of triaging duplicate issues.

4. Supports risk-based prioritization

Not all vulnerabilities carry the same business impact. Effective scanning tools rank risks based on data sensitivity, asset criticality, runtime exposure, and presence of known exploits. This approach allows you to target and fix what matters, reduces alert fatigue, and maintains delivery speed.

5. Actionable reporting

A scanning tool that translates results into contextual details such as vulnerability impact, affected components, severity, or risk score, along with clear, fixable next steps, helps you know what to resolve and how to do it. Comprehensive reports that offer traceability and show historical trends also support compliance and audits.

Automated vs AI-Powered Security Scanning

While both automated and AI-powered security scanning can help you run scans in a continuous loop and with precision, it’s important to understand how each works so you can select the right one.

Traditional automated vulnerability scanning focuses on known patterns, whereas AI adds behavioral intelligence.

Automated Security Scanning

This mainly follows predefined rules, signatures, and patterns to detect the known vulnerabilities across your apps, APIs, and dependencies. These scans can typically run on fixed schedules or be triggered within your CI/CD workflows. They are repeatable and predictable.

Automated scanning is primarily:

Rule-based and signature-driven: Automated scanners match app behavior, code patterns, or configuration states against known vulnerability signatures to identify potential security risks. This method can be extremely efficient for detecting known issues, including previously reported bugs or recurring errors at scale.
Executes scans automatically on schedules or triggers: With the help of automated tools, you can schedule or trigger security scans after code commits, pull requests, or deployments. This will ensure continuous security coverage and allow your team to flag issues the moment they happen with very little manual effort.
Produces detailed findings: Automated scans uncover low-severity as well as high-impact issues, so you get complete visibility into your security status. You can use filtering or prioritization to better assess risks and plan for resolution.

Since automated scanning depends heavily on static rules and signatures, it has a limited understanding of runtime execution context and real user flows, which can make it tough to assess if a finding is actually exploitable. And this can lead to higher false positive rates and low-impact alerts.

AI-powered security scanning

Scanning tools powered by AI don’t simply rely on static, rule-driven detection. They can learn from app activity, runtime signals, and user interactions to identify critical issues and give you insights more aligned with real attack patterns.

Learns from historical test runs and system changes: AI-driven tools continuously learn and refine scans based on past test results, code changes, and deployment patterns. AI models assess how previous issues were introduced, recognize recurring risk patterns, and then adapt security checks to surface risks that are relevant to the current state of your app.
Vulnerability assessment with context: AI helps you correlate detected vulnerabilities with executed code paths, test scenarios, and environment configurations. So rather than producing findings in isolation, it evaluates if the code executed has potential vulnerabilities and under what conditions.
Prioritizes risks based on actual exposure: AI combines real exposure signals such as loose access controls, user telemetry, and excessive permissions to focus mainly on flagging security issues that can potentially affect critical user paths like login, authorization, payment, and checkout.

Using a Security Scanning Agent to Protect Test Execution and Runtime Environments

Our Security Scan Agent is specifically designed to help you identify security risks during test execution by scanning apps for known vulnerabilities and insecure patterns before they reach production.

This agent typically activates at predefined checkpoints to ensure security validation happens consistently and in a predictable way. It runs as a part of your CI/CD pipeline, before release or deployment milestones, and after changes that affect authentication, authorization, or data handling.

With the help of this agent, you can scan for:

Insecure configurations and exposed endpoints
Authentication and authorization weaknesses
Data handling risks related to sensitive information
Known vulnerability patterns mapped to industry standards
Common app vulnerabilities Moreover, with every security finding, you get the test or execution step where it was detected, severity indicators, supporting evidence, affected endpoints or configurations, and execution environment and runtime context.

This agent automatically runs scans alongside tests, embeds security checks directly into execution workflows, and works with other quality and analysis agents to keep your test data secure, support DecSecOps practices without replacing dedicated security processes or tools, and keep all findings visible, traceable, and reviewable.

Best Practices Before Implementing Security Scanning in CI/CD Pipelines

For building and maintaining trust in your apps, it’s important that you know how to efficiently enforce security scanning practices in your delivery pipelines. This helps your team shift security left and identify potential issues at stages when it’s less complex and expensive to fix.

This is only effective when supported by continuous vulnerability scanning that runs across environments, not just during release windows.

When integrating security scanning, ensure it supports enterprise-grade governance, including role-based access controls, security policies, and consistent scanning and control across environments.

You must also make sure you get audit-ready execution evidence that includes clear proof of what was scanned, when, and what the outcomes are. Keeping these points in mind will make your security assessments and compliance far less stressful.

At this point in the testing lifecycle, an AI software testing agent such as CoTester is used to execute approved application workflows and record how the system behaves under test.

Test runs generate logs, screenshots, and step-level outcomes that remain linked to the workflows being exercised, allowing security findings to be reviewed in the context of real application behavior rather than standalone scan results.

To apply this consistently across environments and delivery pipelines, teams use TestGrid to manage execution control, governance, and visibility. It supports structured testing workflows where security scanning, execution evidence, and audit records remain aligned without relying on parallel tools or manual reconciliation.

If you want to understand how this approach fits into your security and testing processes, book a demo with TestGrid to review how audit-ready security validation can be applied across your pipelines.

This blog is originally published at TestGrid

Complete Guide to Mobile Device Management Testing: Process, Challenges & Solutions

Irina Kozlova — Tue, 23 Jun 2026 17:30:11 +0000

Mobile devices today are the primary gateway to enterprise apps. It allows your employees to access business systems, perform tasks, and stay active from literally anywhere.

As of 2026, the global mobile device management market stands at $11.11 billion and is expected to reach around $26 billion by 2031.

But as the market is growing, ensuring critical business apps work reliably on MDM-enabled devices is becoming important because teams across departments are relying on these managed devices to run critical business operations and share confidential information.

However, since MDM devices have strict security, compliance, and access controls, QA teams might find testing on these devices tricky.

In this blog, we’ll learn about the fundamentals of MDM, its impact on app functionality, and the strategies your QA teams can use to test effectively in these managed environments.

Streamline testing across MDM devices, enterprise networks, and real-world environments with TestGrid.

What Is Mobile Device Management (MDM)?

Mobile device management (MDM) is a practice where organizations manage and secure mobile devices, such as smartphones and tablets, remotely to ensure their business data and network stay protected.

There are designated platforms that offer you MDM services and allow you to handle application management, endpoint management, synchronized scheduling, and inventory management.

Key Components of Mobile Device Management

Device tracking – MDM platforms allow you to monitor your enrolled devices via GPS tracking and identify non-compliant devices, troubleshoot issues remotely, and lock or wipe devices in case they’re lost or compromised

Application security – Many MDM solutions enable you to secure your apps with techniques like app wrapping and containerization. With these, you can enforce authentication requirements, restrict copy-paste or file-sharing actions, and prevent sensitive app data from being stored insecurely on devices

Mobile management – One of the primary features of MDM is that it helps your team efficiently provision, configure, and maintain enterprise mobile devices at scale. This can include deploying operating systems, installing business apps, managing updates, and data backup and restoration

Identity and access management (IAM) – IAM features like single sign-on (SSO), multifactor authentication, and role-based access controls let you regulate your device and app access based on identity, and ensure that only authorized users have access to enterprise data

Endpoint security – with robust MDM, you can secure multiple endpoint types, including smartphones, wearables, and IoT devices that are connected to your corporate networks, and implement security measures like antivirus protection, URL filtering, cloud security, and incident response management

Why Businesses Use Mobile Device Management

Modern businesses depend on distributed workforces, BYOD models, and enterprise apps, and they want to secure and manage their devices at scale with very little operational overhead.

Mobile device management allows teams to regulate their enterprise environment centrally and ensure devices have secure access and are compliant.

Mobile device management system helps you:

Manage corporate risk by enforcing security restrictions that directly influence your app’s permissions, authentication, and data-sharing
Improve your device lifecycle management by allowing you to provision, update, retire, and repurpose enterprise devices remotely at scale with minimal manual involvement
Increase visibility across your enterprise via monitoring and reporting; this actually helps you identify policy violations and potential security risks promptly
Enable secure remote workforce operations with enterprise VPNs, proxy networks, and restricted connectivity

Challenges of Testing Apps on MDM-Enabled Devices

1. Compliance-Based Feature Failures

Since mobile device management systems enforce compliance policies, they can affect how your mobile apps function on these managed devices and make it tough for you to test reliably.

Some features of your apps may fail in case a device doesn’t meet conditions such as minimum OS requirements, encryption status, VPN enforcement, or root detection checks. E.g., conditional access rules may block authentication.

2. Limited Access for Debugging

MDM restrictions can limit the level of device access that your testers and developers have when resolving issues. If the policies disable USB debugging, block developer options, restrict log collection, or prevent screen capture, then you might find it hard to inspect crashes, capture diagnostics, or reproduce issues.

3. Network Complexities

Your managed devices mostly work under enterprise-controlled network configs like per-app VPNs, proxy servers, certificate-based authentication, traffic filtering, and private gateways.

These can cause login failures, API timeouts, or broken push notifications, and as a result, your QA team might struggle to isolate root causes and assess where exactly the issues came from.

4. App Installation and Update Restrictions

Some MDM policies can govern how you install and update your app. Your organization may allow installations through enterprise app stores, enforce silent deployments, block sideloading, or delay OS and app updates till your devices meet defined compliance requirements.

Now, these restrictions may keep your instances secure, but version inconsistencies across test devices may lead to unstable test environments and unexpected regressions.

How Does MDM Affect Your App Behavior

Because of the security and authentication layers of mobile device management, your app can function very differently from the way they normally perform on consumer devices.

Enterprise battery optimization and process management policies may reduce the background activity in your apps
Controlled storage access may affect the file downloads, uploads, local caching, and document sharing features
Notification rules might impact notification delivery, foreground alerts, or background sync
Enterprise authentication policies may need additional login prompts or rules, or forced re-authentication
Clipboard restrictions can stop your users from copying OTPs, credentials, or business data
Location access settings can interfere with geofencing, live tracking, and check-in systems in your apps

How Mobile Device Management (MDM) Works

1. Device Enrollment and Registration

The typical MDM workflow starts when you enroll a device in your organization’s management systems with the help of methods like QR-based onboarding, zero-touch enrollment, Apple Business Manager, or Android Enterprise provisioning.

You register the device with the MDM server and then connect it with your organizational policies, user identities, and security settings.

2. Policy Enforcement from a Central Console

After you have enrolled the devices, admins use a centralized MDM console to configure and distribute policies across the device fleet remotely.

These policies can generally be associated with governing password requirements, app permissions, encryption settings, VPN and network access, and hardware permissions such as camera or Bluetooth usage.

The MDM platform’s server syncs these rules with your devices so you can manage them and respond to security incidents without physically accessing the devices.

3. Enterprise App Deployment

Most MDM platforms allow you to deploy your custom apps on managed devices. You can easily push your app updates, set up policies specific to your apps, revoke access, or even remove apps from these devices if needed.

This flexibility helps your testers check app functions under real enterprise deployment conditions before release.

4. Continuous Monitoring

MDM platforms help you monitor your devices and track compliance status, security posture, and policy adherence in real time, which makes it easy for your admins to spot rooted devices, outdated OS versions, or unauthorized app activity from a unified dashboard.

How to Test on MDM-Enabled Devices? A Stepwise Process

This detailed process will help you address the challenges that we discussed about testing on MDM-enabled devices and ensure seamless app performance under enterprise management environments.

1. Assess Your Enterprise Requirements

You need to start with understanding how your organization will be using the mobile device management system and which enterprise controls may potentially affect your app behavior.

This can include reviewing factors like device ownership models (BYOD, COPE, or corporate-owned), supported operating systems, compliance obligations, authentication mechanisms, and your security requirements.

This step will help your QA team design test scenarios that reflect how and under what conditions your users access the app.

2. Build an MDM Test Matrix

The impact of the MDM policies can differ across operating systems, device types, enrollment methods, and management configurations, which is why you need to create a test matrix to map the combinations that are most relevant to your app.

This can include different iOS and Android device versions, user roles, work profiles, compliance state, and network conditions.

MDM Test Matrix Example

3. Set Up Your Test Environment

Build a test environment that closely matches the enterprise conditions under which your apps would operate. This means configuring the same management controls, access settings, and security policies.

Enroll your test devices into the MDM platform you want to use via the appropriate provisioning method
Set up device ownership models like BYOD work profiles, fully managed devices, or supervised devices
Apply the same security and compliance policies as the production environment
Create and assign test user accounts with role-based access permissions
Enable enterprise services, including identity providers, SSO, VPN, Wi-Fi, certificates, and proxy settings Deploy your app through the mobile device management system Confirm policy enforcement and compliance status, and ensure that all your device restrictions are active before you start testing

4. Test Your Core Functional Flows

The next step is to test your app’s user journeys and ensure they work as expected under MDM management.

Here, you should focus more on the critical business features and workflows such as user login and authentication, onboarding, data entry, transaction processing, file handling, and notifications.

The main aim is to make sure that all important business functions are accessible and reliable for enterprise users.

5. Test Under Different Policy Combinations

Different departments and user groups in your organization may have different compliance requirements and unique restrictions.

E.g., your sales team may be allowed to use the company CRM app on personal devices, but the finance team might be required to use corporate-owned devices that have stricter security requirements.

This is why you should test your app across multiple policy combinations to check how access rules affect core workflows.

6. Automate Efficiently

Automation can help you improve your test coverage by scaling tests when enterprise controls and device versions frequently change.

You can leverage frameworks like Appium and integrate the tests into your CI/CD pipelines to automatically test on MDM-enabled devices after every release or policy change.

Here’s a pro tip: automate the repetitive flows first, like installation, login, authentication, and regression testing. One-time enrollment processes, complex certificate provisioning, and exploratory testing should be tested manually because they’re environment-specific and can be tough to automate reliably.

Test Apps Securely on MDM-Managed Devices with TestGrid

For testing apps on MDM-managed devices, your QA team needs secure test environments where they can verify enterprise policies, managed configurations, certificates, and access controls without compromising sensitive business and user data.

TestGrid is an AI-powered software testing platform that offers you private device labs and on-premise infrastructure so you can test your apps on real devices within your own network, firewall, or enterprise VPN.

The platform supports end-to-end manual as well as automated testing, including functional and non-functional checks throughout your development cycle.

With TestGrid, you can:

Run tests on VPN-enabled setups and ensure GDPR and HIPAA compliance
Enforce enterprise policies, schedule updates, and assign devices to teams securely inside your private lab
Add, replace, or retire devices without interrupting your ongoing test cycles
Provision devices, monitor device usage in real time, and keep your on-prem infrastructure secure, compliant, and always up to date
Leverage TestGrid’s BYOL to create safe and controlled test environments for MDM testing This blog is originally published at TestGrid

Audio Testing for AI Chatbots: Key QA Techniques and Best Practices

Irina Kozlova — Sun, 21 Jun 2026 16:50:35 +0000

Chatbot experiences have now changed from textual conversations to voice-driven interactions, and the reason is pretty obvious.

Voice-enabled chatbots help your users interact more naturally and hands-free, just like talking to a person, and get real-time assistance faster.

The global chatbot and voice market, valued at $10759.5 million in 2026, is expected to grow to $29046.99 million by 2035. And AI chatbots are dominating here with nearly 60% of the market share.

Although voice-based chatbots are making it easy for customers to resolve queries, testing them poses a new set of hurdles for QA teams because of variables like speech patterns, accents, background noise, device behavior, and volatile network conditions.

In this blog, we’ll know how QA teams can approach end-to-end testing for voice-enabled chatbot experiences across devices and conversational workflows.

Analyze the audio quality of chatbots across user interactions with TestGrid.

Why Audio Testing for Chatbots Needs More Than Text-Based QA

1. Hardware and channel variability

Text input gives your chatbot a clean request. You type a sentence, the app receives it, and your tests check how the chatbot responds.

But with voice inputs, audio testing becomes essential because you must verify whether the microphone activates correctly, whether the browser or app has permission to capture audio, and whether the audio signal is clear enough for accurate speech recognition.

These factors can lead to clipped, muted, or delayed audio and cause chatbot failures

2. Speech recognition may alter user intent

Voice-enabled chatbots primarily depend on the transcripts they receive to generate responses. So, if a speech-to-text converts your user’s words incorrectly, then the chatbot may end up processing a request that was not even made.

E.g., ‘block my card’ can become ‘unlock my card’. And ‘cancel my flight’ can become ‘change my flight.’

Your QA team needs to assess transcript accuracy by first checking if the general sentence was captured correctly, and second, by thoroughly inspecting the critical items like names, dates, amounts, addresses, account numbers, OTPs, medicine names, airport codes, and booking IDs.

3. Unpredictable real-world speech conditions

Real users may interact with your chatbots from cars, homes, offices, hospitals, airports, call centers, shops, and public transport. They may speak quickly, repeat themselves, pause mid-sentence, or mix languages in the same query.

Now, these conditions (accents and pronunciation differences) can lead your chatbot to miss important information or respond to the wrong phrase. This is why your test data must reflect real scenarios like traffic noise, low volume, regional accents, and voice modulations.

4. Voice responses are harder to test than text

Since text responses are visible, your users can easily read them again, copy information, scan for details, and find mistakes. But with voice responses, factors like timing, pronunciation, pacing, and memory come into the picture.

Your testers have to verify if the chatbot can speak clearly, use the right pronunciation, keep the responses short so users can follow, and avoid cutting off important information.

You also have to check if the user can stop the chatbot and ask it to repeat information or switch to text if needed.

5. Voice errors in high-risk workflows

Voice errors, such as an incorrect transcript or low-confidence intent classification can affect your payments, cancellations, account changes, appointments, claims, bookings, fraud reports, or identity verification.

Therefore, to avoid that, you need to assess how your chatbot behaves before it takes risky actions. You have to make sure it confirms critical details, asks for clarification when confidence is low, and routes your user to a safer path in case the request is unclear.

E.g., before cancelling a flight, your chatbot should repeat and confirm the passenger details, date, and destination.

How Would You Identify the Chatbots That Need Audio Testing

1. Voice-enabled chatbots for mobile and web apps

Chatbots in mobile and web apps need testing across the full user path (your user taps a microphone button, speaks a request, and receives a text or spoken response).

Since these chatbots depend on browser permissions, app permissions, device microphones, speech recognition, and intent detection, you need to check whether it can handle denied access properly, or if the mic prompt permission shows up at the right time.

Make sure you test the same voice request and verify transcription, intent, flow progression, and final response across browsers, device models, and operating systems.

2. IVR-style chatbots which handle customer calls

In IVR-style chatbots, the entire interaction with your user happens within a phone session, where the bot collects information, routes users, answers common questions, and transfers calls to human agents if needed.

Because phone audio may get compressed or noisy due to poor signal quality, here, you need to test audio capture, prompt timing, user silence, background noise, repeated inputs, and incorrect routing.

3. Real-time AI voice agents

AI voice agents have to work with open-ended speech, multi-turn context, spoken responses, and interruptions. So, your user might ask a question, correct a detail, change the task, give multiple requests in a single interaction, or barge in when the answer is too long.

Therefore, your tests need to verify that the chatbot is able to maintain conversational context and state across multiple turns.

Say, your user requests ‘book an appointment for Monday’ and then immediately adds ‘make it after 4’, your chatbot must connect the second input with the first one.

4. Multimodal chatbots

Multimodal chatbots usually combine voice, text, buttons, images, forms, docs, and visual prompts, which is why thoroughly testing them is very important.

If your user inputs a voice prompt to make a flight change and then taps on a date on screen, your chatbot must be able to correlate both inputs within the same booking flow.

Your tests for multimodal chatbots should ideally cover mode switching, state retention, partial inputs, and recovery from errors.

5. Chatbots that accept voice notes and recorded audio inputs

Some chatbots depend on recorded audio messages to generate a response rather than real-time speech. You’ll find them generally in messaging apps, support portals, healthcare intake flows, field service tools, and customer service channels.

Since audio here gets uploaded as a file which the chatbot processes, you have to test file uploads, format support, duration limits, compression effects, transcription accuracy, and retry actions.

You should ensure that the chatbot can function with short clips, long recordings, or noisy uploads, and still extract the correct information.

6. Contact-center chatbots

This category of chatbots mostly works in the background and supports human agents in solving customer queries.

They may assist via transcription, summarization, routing, suggested responses, compliance prompts, and after-call notes. So, errors here can affect both the customer and the human agent’s next steps.

Therefore, you should check speaker diarization, terminology, names, numbers, product references, complaint categories, and escalation signals to ensure that your chatbot accurately captures the call to help the agent solve customer queries efficiently.

How QA Teams Can Map and Test the Chatbot Audio Pipeline

1. Trace your chatbot’s audio journey

The first thing you should do before you start writing test cases for the chatbot is to map the full path your user’s voice takes.

Usually, most user journeys in voice chatbots look something like:

Your user activates their microphone
The app or browser then requests permission, captures the audio, and sends the speech to the recognition layer
The ASR service then converts the audio into a transcript
Your chatbot uses this transcript to detect intent, call backend services, and generate a response
For each of these stages, your testers should define a testable expected outcome. Meaning, if the mic is blocked, then the chatbot should show an explicit recovery message. Or, if the transcript is incomplete, the chatbot must ask for clarification.

2. Classify defects across the audio pipeline

After you’ve mapped the audio journey, next, you need to classify the defects so you can triage faster. Broadly, there could be five classifications of defects:

Capture defect – this happens when the chatbot couldn’t capture usable audio because of mic permission failure, clipped audio, or muted input
Recognition defect – it occurs when the audio is captured but the transcript is wrong or incomplete
Entity defect – here the transcript is mostly correct, but your chatbot collects wrong details (date, amount, account number, or airport code)
Intent defect – this happens when your chatbot selects the wrong goal or cannot identify the intent
Response defect – this defect occurs when the chatbot generates an incorrect response or omits required information
This classification helps you keep your QA, development, speech teams, and product owners aligned, and allows you to track recurring issues like device capture failures or entity extraction gaps.

3. Build a voice test data matrix

The next step is to design a voice test data matrix that will enable you to test chatbot audio scenarios against specific inputs and expected outputs.

For that, you will need to define the user utterance for each chatbot scenario. Then attach that to the audio source speaker profile, accent or language variant, acoustic environment, device, browser, and network profile. Here, you should also add expected responses and pass criteria.

4. Test with real-world speech variations

Challenge your chatbot with scenarios that resemble how your users actually speak rather than just depending on clean audio.

Include low volume, loud speech, fast speech, slow speech, distorted audio, silence, pauses, overlapping speech, and domain terms.

And also, apply conditions that match the chatbot’s industry. If you have a telecom support chatbot, you need to consider call-center noise and poor mobile signal conditions.

Your goal here is to find where exactly your chatbot’s behavior becomes unreliable and under what conditions.

5. Ensure spoken input triggers the correct chatbot intent

Confirm that your chatbot is able to map spoken phrases to the correct conversational action consistently.

Since your users don’t normally follow fixed sentence structures in voice interactions, you should test paraphrased commands (‘book a cab’ vs ‘get me a taxi’), filler words, and conversational speech patterns, and ensure that the chatbot can interpret the correct intent in all cases.

6. Examine multi-turn chatbot conversations

When users change topics, correct themselves, or ask follow-up questions in the middle of an interaction, the chatbot should maintain conversational continuity without losing context.

For multi-turn audio flows, fallback testing is important. Even if your chatbot cannot understand one turn, it should preserve relevant information that it collected earlier.

7. Test confidence thresholds before the chatbot proceeds

Set predefined ASR and intent-classification confidence thresholds and check how your chatbot behaves when the confidence is low.

You can test this by feeding ambiguous audio, partial commands, or code-switched language inputs and seeing if the chatbot proceeds or escalates the request to a human agent.

How to Validate, Automate, and Scale Your Chatbot Audio Testing

1. Review chatbot audio outputs and signal quality

For efficient audio output testing, you must include objective checks in addition to human listening.

Reference and recorded audio comparison can help you spot clipping, distortion, decoding errors, signal degradation, excessive noise, and audio artifacts.

This check can be particularly useful for chatbot voice prompts, spoken confirmations, alerts, disclaimers, and text-to-speech responses.

Best practice
You can maintain baseline reference audio files and assess your chatbot’s playback quality across multiple devices, formats, and network conditions to detect audio degradation promptly.

2. Measure your chatbot’s voice latency

Measuring end-to-end latency in chatbots means checking how long the system usually takes to capture audio, convert speech to text, detect intent, call backend services, generate the answer, and play it back to the user.

Your users expect immediate responses. So, if there are long pauses, the user may have to repeat the request or assume that the chatbot failed.

Best practice
You should separate latency by stage. If your chatbot normally takes three seconds to respond, but it took six, you need to check if the delay happened because of speech recognition, the chatbot model, a backend API, text-to-speech generation, or playback. This way, you can diagnose and fix issues better.

3. Automate tests across real devices and usage conditions

Since audio chatbot behavior can change across device models, OS, browsers, and audio accessories, you must test on the same device and browser matrix that your users rely on.

Include the latest iOS and Android devices, recent OS versions, mobile browsers, desktop browsers, and audio devices like speakers and headphones.

Then create automated tests that help you evaluate chatbot response, fallback behavior, expected transcript, and escalation paths.

Best practice
Build a regression test set with audio files for common intents, critical entities, accents, and high-risk workflows, and reuse that after changes to detect issues across different browsers and devices.

4. Capture the right evidence for chatbot audio defects

For efficient defect resolution, you need to ensure that your testing system is capturing detailed evidence so your testers can identify what failed and where.

You should collect original audio files or input source, the transcript, confidence score, device, OS, or browser where the defect occurred, network profile, session recording, screenshot, and backend logs, where available.

Best practice
Try to standardize audio defect reporting with mandatory logs, transcripts, environment details, and session recordings. This will allow your team to reproduce issues consistently and convert confirmed defects into reusable regression test cases.

5. Set up release gates for chatbot audio quality before production

Your audio chatbot has to meet quality gates before release. These gates should measure intent accuracy, task completion rate, fallback rate, correction rate, escalation rate, response latency, audio dropout rate, device coverage, and accessibility compliance .

Best practice
For high-risk workflows that affect money, identity, health, booking, or claims, use stricter thresholds. If you are testing audio chatbots in banking, payments, healthcare, or insurance domains, set lower acceptable latency limits, mandatory confirmation prompts, and reduced fallback tolerance.

Test Chatbot Audio Where Users Actually Speak

Audio testing for chatbots has to cover the full voice journey: microphone access, speech recognition, Intent classification , response quality, latency, fallback handling, and release readiness.

A chatbot can pass in clean test conditions and still fail when users speak through low-quality mics, switch to Bluetooth, pause mid-sentence, or give critical commands in noisy environments.

TestGrid is an end-to-end testing platform that helps you validate those conditions directly on real iOS and Android devices.

You can stream microphone input into a device session to test interactive chatbot flows, or upload pre-recorded audio files to run repeatable regression tests with the same input across releases.

This helps your QA team check whether spoken commands are captured correctly, transcripts trigger the right chatbot intent, and voice responses behave as expected across device and OS combinations.

You can also use TestGrid to test chatbot audio across device models, OS versions, audio accessories, and network conditions, so your team can catch issues like muted input, delayed responses, routing failures, playback problems, and inconsistent behavior before users face them.

For QA teams building or validating voice-enabled chatbots, TestGrid gives you the real-device audio testing setup needed to test faster, reproduce defects better, and release chatbot experiences with higher confidence.

This blog is originally published at TestGrid

A Complete Guide to Enterprise Application Testing: Types, Process & Best Practices

Irina Kozlova — Wed, 17 Jun 2026 16:16:26 +0000

Enterprise application testing is the structured process of validating complex, interconnected business software such as ERP, CRM, SCM, and HCM systems to ensure they perform reliably, integrate seamlessly, and support critical operations without failure.

Today’s enterprises run on a network of these apps. The enterprise app integration market sits at $20.34 billion and is projected to reach $55.62 billion by 2034, a clear signal of how deeply embedded these systems have become in daily operations.

And for good reason. Your customer data flows through CRM platforms, financial transactions run on ERP systems, and logistics depend on supply chain tools. Each of these apps operates within a web of data pipelines, APIs, user roles, and microservices, all tightly connected. A failure in one doesn’t stay isolated; it cascades.

The stakes are real. Enterprise application downtime costs thousands of dollars per minute. For workflows like order processing or payroll, even a short outage can trigger financial losses, compliance violations, and broken customer trust.

That’s why enterprise application testing isn’t optional; it’s foundational. This guide will walk you through how to test your enterprise apps end-to-end, with a methodology and best practices your team can act on.

What Is Enterprise Application Testing?

Enterprise application testing is about ensuring that the software systems and apps that run your critical business processes, like enterprise resource planning, customer relationship management, or product lifecycle management, function as expected, integrate easily with other systems, and meet business goals. Unlike standard software testing, enterprise application testing must account for multi-system dependencies, high data volumes, role-based access, and continuous integration across business units.

Why is End-to-End Enterprise Testing Important?

Enterprise apps mostly support long chains of business processes, like an order going from sales to inventory, billing, and fulfillment. So naturally, testing this entire flow is important to maintain operational continuity. Inconvenience at any point, no matter how minor, can affect your revenue.

These apps have to process high data volumes and requests, and work with confidential user data; therefore, testing should include functional, performance, security, and usability checks

What are the Types of Enterprise Applications and What Needs Testing

1. Customer Relationship Management

CRM systems help you manage business interactions with your prospects or customers throughout the sales lifecycle. With the help of these systems, you can capture leads, assign them to your sales rep, track communication, forecast revenue, and maintain customer records.

CRM Testing – What to test:

Consistent handling of creation, deletion, and updates of customer data across systems
Fluid interface and smooth usability on desktops, mobiles, and tablets
Consistent sync between different CRM modules and external databases to prevent data corruption
Secure access controls to safeguard confidential user data and ensure users see only data that’s relevant to their roles

2. Enterprise Resource Planning

You run your core business operations via these platforms. These connect workflows for finance, inventory, procurement, manufacturing, and more, and include processes such as order processing, resource planning, financial reporting, and management of operational data.

ERP Testing – What to test:

Complex rules, policies, and regulations embedded in workflows apply correctly
Flawless information flow between finance, HR, inventory, and procurement departments
Performance assessment under heavy loads to make sure workflows are stable and responsive
Systems should adapt accurately to regional languages, multi-currency operations, and legal requirements

3. Supply Chain Management

Planning, managing, and optimizing the flow of goods from suppliers to customers is what SCM systems help you do. Workflows here usually include demand forecasting, procurement planning, inventory tracking, shipment scheduling, and warehouse coordination.

SCM Testing – What to test:

Electronic data exchange processes to ensure seamless interactions with suppliers
Maintain a correct reflection of stock levels across warehouses and sales points
Shipment status and delivery timelines must be tracked and updated precisely Interactions with connected devices like radio frequency identification readers and sensors should be free from friction for efficient inventory and asset management

4. Human Capital Management

These systems allow you to manage talent, compliance, and employee development, and involve some critical workflows like recruitment, onboarding, payroll processing, benefits administration, training programs, and performance reviews.

HCM Testing – What to test:

Systems should adhere to company rules for attendance, leave management, performance evaluations, and compensation
Intuitive approval processes for leave requests, expense reimbursements, and employee onboarding tasks
Correct computation of salaries, benefits, taxes, and deductions
Employees should have the flexibility to easily access HCM features on their mobile devices

5. Business Intelligence and Analytics

Business Intelligence platforms help you turn raw operational data into insights for better decision-making. The systems here collect data from many sources, run queries, build rich dashboards, generate reports, and analyze trends for your business.

BI and Analytics Testing – What to test:

Data must be properly ingested, transformed, and loaded from different sources
Fast loading of dashboard widgets and reports for large volumes of data
Tables, graphs, and charts should correctly show the data
Metrics should have consistent calculations, aggregations, and data transformations

6. Other Enterprise Solutions/Custom Apps

Many teams build their own custom enterprise apps to match unique workflows with internal processes, partner portals, document management, and other industry-specific operations.

Custom Enterprise App Testing – What to test:

Data exchange with third-party or external platforms like CRM, ERPs, internal APIs, or payment gateways has to be reliable
Custom workflows like approvals, document routing, or internal request handling must follow the intended business logic
Make sure only relevant users can see, delete, or modify critical and sensitive data
Custom apps should have the capability to scale operations when your users grow

Enterprise Application Testing Methodology

Requirements and traceability planning: Decide first what exactly you need to verify. Coordinate with your team to assess the business requirements, user stories, and system specifications, and then map them to test scenarios. Usually, at this point, you should create a requirements traceability matrix that will help you link every requirement to a corresponding test case so that nothing gets missed while testing.
Test data and environment setup: Enterprise apps regularly communicate with external platforms, databases, APIs, third-party services, and cloud infrastructures. That’s why you have to make sure your test environments are realistic.

Test with data that reflects real business scenarios, customer records, financial transactions, or inventory status. Also, your test environment should have proper authentication, encryption, and data masking mechanisms to protect sensitive information.

Select the right testing tool: AI testing tools can take a lot of manual effort off your team. You can automatically analyze app behavior, generate relevant test cases, spot risky areas in code, and adapt tests to your changing workflows.

Look for an AI tool that has:

Self-healing automation to update locators and scripts dynamically when UI elements alter
Ability to generate tests based on app flows and usage patterns
Smart test prioritization for identifying risky areas and running the critical tests first
Cross-platform and device support to verify business workflows on mobiles, web, and desktops
CI/CD integration: It’s a given that enterprise apps will need updates constantly as your business expands and your user base grows. Therefore, to match the pace of frequent releases, integrate the testing tool with your CI/CD pipeline to trigger validation checks, catch defects immediately, and maintain stability in releases.

Test automation: Automating your tests is critical, particularly for enterprise apps, because they have multiple workflows, integrations, and user scenarios that are practically extremely tough to test manually.

These are some important tests that you must automate with the help of AI tools.

Enterprise functional and non-functional testing

UAT and stakeholder validation: It’s important that before you release the changes, they are verified by people who are actually going to use your app. User acceptance testing is how you do it. Here, your business users, product owners, and even real users access the app to confirm if it functions well and is ready for production.

This final phase is more about business alignment and less about technical defects.

Complexities of Enterprise App Testing and How to Tackle Them

Rapid release cycles and DevOps pressure: Your enterprise may sometimes have to ship multiple times a week. And with such fast release cycles, testing windows get shorter, which can result in missed defects and edge cases. This can affect user experience post-release.

Best practice
Incorporate testing into your CI/CD pipelines so that code gets tested immediately after commits. Another tip is to prioritize testing based on business impact and failure risk so you can ensure the released version doesn’t affect critical workflows.

Complex architectures and interdependencies: Since enterprise apps have interconnected APIs, internal tools, external services, and databases, one minor change in a module can affect multiple dependencies and impact workflow elsewhere in your app.

Best practice
You don’t have to equally test everything. Just focus on workflows that span multiple systems like order processing, payments, or user provisioning. These cross-system paths usually carry the highest business risk.

Test data management and compliance constraints: Financial data, user records, and employee details are sensitive by nature and accessed continuously by your enterprise apps. If you use real production data for testing, it can lead to unintended data leaks and security risks.

Best practice
You can mask the sensitive fields or generate synthetic data sets that represent realistic scenarios. This will help your team cover the edge cases and complex workflows while maintaining data privacy and staying compliant with regulations.

Environment instability and configuration drift: Configuration changes, dependency updates, or manual patches can cause your test environment to drift. And these differences between QA, staging, and production infrastructures can create inconsistent test results.

Best practice
A practical way of overcoming this challenge is to use infrastructure-as-code to define environments through version-controlled scripts rather than manual setup. This will allow you to keep test, staging, and production environments provisioned with the same configurations.

Optimize Enterprise App Testing with CoTester

CoTester is an enterprise-grade AI testing agent built to support teams who deal with large, complex business apps, with constantly changing modules, new integrations, and tight release deadlines.

This agent covers testing for almost all the major enterprise apps and allows you to instantly create tests from user stories, auto-heal locators when UI elements change, run tests across real browsers and devices, get live feedback, and retain control throughout the process.

Here’s a quick overview of CoTester’s abilities:

Capture real Salesforce app flows step by step directly from live browsers, automatically convert each interaction into a test step, and analyze failures using step-by-step execution history
Convert your SAP stories, functional specs, and requirements into structured tests, review and refine steps, and easily run automated tests across the SAP GUI
Validate Dynamics 365 business scenarios end to end, adjust locator resolution as Microsoft updates forms, fields, and page loads, and ensure test accuracy across role-based behavior
Create tests for Zoho apps directly from real user interactions, monitor each step, look for bugs, and identify failure points with execution context
Upload your NetSuite user stories or Jira change tickets and automatically turn functional intent into tests, schedule test runs to align with your release cadence, and review expected outcomes
Auto-generate tests from your ServiceNow workflows, run regression tests every time you upgrade, and leverage the extensive pre-built library that covers all the core modules To unlock all the advanced features of CoTester for your enterprise app testing, request a free trial today.

Conclusion

Enterprise application testing isn’t a one-time checkpoint; it’s an ongoing discipline that keeps your most critical business operations stable, secure, and ready to scale. Whether you’re managing CRM pipelines, ERP workflows, supply chain logistics, or HR systems, the risks of inadequate testing compound with every release cycle.

The good news is that with the right methodology, realistic test environments, and AI-powered tools like CoTester, your team can move faster without sacrificing quality. Start by mapping your highest-risk workflows, integrating testing into your CI/CD pipeline, and let automation handle the repetitive heavy lifting so your engineers can focus on what matters most.

This blog is origianlly published at TestGrid

How to Test AI Applications: Complete Guide for QA Teams in 2026

Irina Kozlova — Tue, 26 May 2026 16:28:43 +0000

The growth of artificial intelligence apps is hard to ignore. Almost every app that you use today has some level of AI integration. It could be a chatbot, an AI-powered search, a recommendation engine, or a voice assistant.

As AI becomes more embedded in everyday products, ensuring these systems work accurately and reliably becomes critical. This is where testing AI applications comes in, evaluating how AI models behave, the quality of their outputs, and how reliably they perform across different scenarios.

The global AI apps market is expected to touch $26,362.4 million by 2030, expanding at a CAGR of 38.7% from 2025.

But even though adoption is accelerating, an IBM report shows that 13% of organizations have faced breaches in AI models or applications.

Businesses may be ready to invest in AI, but they are not always fully equipped to test and manage these systems effectively.

This guide will walk you through the complete process of testing AI applications, including strategies, tools, challenges, and best practices.

Why Testing AI Applications Requires a Different Approach
Testing AI applications is quite different from how you test traditional software apps.

Traditional apps normally follow predefined rules and give consistent outputs. But AI apps are non-deterministic, which means they can generate different results for the same input. The performance of AI models depends largely on training data. So, poor quality data directly affects responses.

Apart from this, since AI decisions can be black-box in nature, they raise concerns about ethics and explainability. These complexities make testing AI applications a lot trickier.

Properly testing AI helps you:

Improve the reliability of responses so your users can get outputs that they can trust and depend on
Maintain transparency by making the model behavior easy to explain
Reduce the risk of non-compliance by allowing you to detect data privacy violations and biased predictions
Lower costly production failures that can potentially affect user experience

Types of AI Models Used in AI Applications

1. Machine learning (ML) models

Machine learning models can learn patterns from structured or unstructured data, assess your problem, and then apply techniques like classification, regression, or clustering to make predictions or decisions. You’ll see these models used commonly in spam filters, fraud detection systems, recommendation engines, and risk scoring tools.

The more you expose these models to diverse data, the better they get at generating contextual and accurate responses.

Associated risks

Data and concept drift can reduce model accuracy when real-world data evolves over time
Problems like overfitting or underfitting may lead to poor performance when the model faces unseen data

2. Deep learning models

Deep learning models are a more advanced branch of machine learning. Basically, these models leverage multi-layered neural networks to process complex data like images, text, or audio. Voice assistants, facial recognition, and translation systems are built with these AI models.

Associated risks

Black-box behavior is a big concern linked to deep learning models; it can make it hard for you to understand how the AI is making decisions
Sensitivity to even minor input changes can result in unstable or unexpected outputs

3. Large language models (LLMs)

Most modern chatbots, content generation tools, coding assistants, and search experiences are powered by LLMs. These models are trained on large sets of text data in order to interact in human-like language. Other than predicting outcomes, they also generate responses based on context. Many systems also use techniques like retrieval augmented generation to improve factual accuracy.

Associated risks

Hallucinations can often produce factually incorrect responses
Bias in training data may give skewed or inconsistent outcomes

4. Generative AI models

Gen AI models help you create content like text, audio, images, video, and code via natural language prompts. You’ll find these models in chatbots, image generators, and AI writing assistants.

Associated risks

Outputs generated by these models don’t have clear right or wrong answers; this can create difficulty in evaluation, since quality and relevance are subjective
Testing generative AI applications can be hard because responses may vary even across similar inputs

5. Computer vision models

Computer vision allows your AI app to see and understand visual data from images and videos. You would see the use of these models in medical imaging, autonomous driving, and quality inspection. They mainly extract patterns like edges, shapes, and textures to do tasks such as image classification, object detection, and segmentation.

Associated risks

Elements like lighting, blur, or noise in actual usage conditions may cause performance issues
Occlusion or partial visibility can result in incorrect object detection

6. Agentic AI models

Agentic AI models are capable of doing a lot more than generating responses. They plan, decide, and take action autonomously based on a specific goal. E.g., you can assign a travel AI agent to book a flight, and it’ll compare options, book the ticket, and send you the confirmation.

Another strong feature of AI agents is that they can communicate with other systems and tools to execute tasks and adapt in real time. You can find AI agents in software testing platforms, customer support automation, and cybersecurity systems.

Associated risks

Goal misalignment can lead the agent to interpret wrong objectives and take unintended actions
Security vulnerabilities like data leakage and unauthorized access can happen without proper guardrails in place

Key Strategies for Testing AI Applications

Data validation: How accurate and precise your AI app’s response is depends largely on the data that you use to train your AI models. So before you start training, check the quality, completeness, and relevance of the data. Meaning, you should include real user inputs, missing values, incorrect labels, duplicates, and distribution imbalances so the model is equipped to handle production scenarios.
Red teaming: Red teaming, or also often called adversarial testing, is a form of security testing where you think like an attacker and simulate misleading queries, edge case inputs, and adversarial scenarios like prompt injections to uncover security vulnerabilities or unsafe outputs.

This method is particularly important if you’re operating in areas like healthcare, finance, and government.

Model evaluation: This is one of the AI testing strategies where you assess how your AI apps function during training as well as in real use. Model drift is a critical concept to consider when you’re evaluating AI models. Often, over time, changes in data patterns can cause AI performance and output to degrade gradually. That’s why you should reevaluate and retrain models with fresh data to keep them relevant.
End-to-end AI workflow testing: In E2E testing, you check the entire system, which includes verifying data flows from input to output across pipelines, APIs, preprocessing steps, and AI models. Your goal here is to ensure that when all the components of your app come together, they can work seamlessly in live environments.
Continuous testing: Because of changing user behavior, emerging risks, and model drift, continuous testing of AI apps is a non-negotiable. For this, you should rerun tests, track performance metrics, and evaluate outputs in production regularly. You also need to set up feedback mechanisms and automated retraining triggers so you can detect and address data anomalies and unexpected behavior promptly.
Human in the loop (HITL) validation: Incorporating human judgment in your testing process is critical because aspects like tone, relevance, safety, and contextual accuracy in AI responses can only be analyzed properly by human testers.

Reviewers here usually assess the outputs, flag issues, and then give feedback that can help you improve the future performance of your apps.

If your AI app uses generative AI, then human assessment is very important because subjective quality matters the most here.

7. Integration testing AI systems: AI apps connect with APIs, databases, data pipelines, and third-party services, and therefore, you need to test the integration points between these components. This testing strategy for AI applications basically covers checking if data is passing correctly through the workflows, as well as assessing error handling, data format mismatches, and system compatibility across environments.

Key Areas to Test in AI Applications

1. Regression: Since small changes can also shift your AI model’s behavior, after retraining, data changes, feature updates, or pipeline tweaks, it’s important to run regression tests to ensure no existing functionality was affected. You must check outputs, performance metrics, and edge case scenarios, and track deviations in predictions or responses.

2. Context-handling: Your AI app should be able to understand and adapt to its surrounding environment, like location, user behavior, session history, or time, and respond in a way that aligns with the user’s intent.

Say, if your user says ‘search running shoes’ and then adds ‘show only Nike shoes’, the AI model should correctly link the second request’s context to the original query.

The focus of testing context handling ability is to check if the model can maintain continuity in conversations and avoid contradictions.

3. Usability: Usability of AI apps isn’t just about how intuitive the UI design is. You also assess the clarity of responses, ease of correction, and how easily your users can navigate the system. Since responses can, at times, be unpredictable, you should check if users can understand the outputs and easily recover from errors.

4. Bias and fairness: Models should treat all users equally, regardless of age, race, gender, or geography. But even well-trained data can have hidden biases and imbalances. You need to test if outputs across demographics and scenarios are skewed or possess discriminatory behavior. And for that, you have to monitor fairness metrics like demographic parity, equalized odds, and proxy attributes.

**5. Goal alignment: **Autonomous or agent-based AI apps generally make decisions over multiple steps, and there’s a possibility that the model strays away from the intended objective. These are some ways you can actually assess if your AI app is successful in fulfilling a goal:

Measure the task success rate to verify that the AI model achieves your expected outcome
Collect feedback from your users, so you can understand if AI is giving responses that align with what they’re looking for
Design multi-step flows and track whether the model can follow the correct steps, avoid unnecessary steps, and reach the goal

How to Test AI Applications: Step-by-Step Process

1. Define test objective and success metrics

The first step of testing AI applications is to outline what exactly it is that you want to test. Say you want to assess if your AI app can show relevant results when a user searches for a product. So, you define an objective like ‘ensure the model can suggest relevant products based on user query and behavior.’

The next part is to map out success metrics that’ll help you check that the model can meet the objective. You can monitor accuracy, precision/recall, extraction accuracy, latency, or task completion rate as benchmarks for success.

2. Prepare test data

When you’re designing input data for testing, your focus should be to include normal cases (clean data), edge cases, and rare scenarios. This means you need to use long queries, boundary values, and noisy inputs.

In this stage, you should also do a detailed audit to identify if there are any mislabeled entries, missing values, imbalanced classes, or unrepresentative sampling that can potentially lead to biased responses. Methods like data rebalancing or augmentation can help you improve representation.

3. Perform multi-level testing

Now, you need to thoroughly test your AI app, find issues, and resolve them before release. You can use different types of tests, but some of the most critical ones are:

Unit testing – this will help you spot issues like incorrect data preprocessing, broken feature transformations, and errors in individual functions
Model testing – problems like low accuracy, bias, overfitting, or unstable predictions can be caught here
System testing – this will allow you to detect latency, failure handling gaps, and inconsistent outputs
Pro tip: You can implement test automation for unit and system testing, and manually explore the edge cases and model behavior for comprehensive coverage.

4. Deploy and monitor

Deployment doesn’t mean your testing process is complete. In fact, after deployment, you must monitor how your app is performing in the production environment. For that, evaluate latency, error rates, and data drift in real time.

Make sure you set up alerts and automated feedback loops to detect anomalies and performance degradation early.

5. Keep refining and improving

Your AI app needs regular updates to stay relevant. So, you have to fine-tune prompts, enhance features, expand test coverage, and analyze failure patterns to ensure the model can adapt to changing data, produce fewer errors, and improve responses over time.

Best Tools for Testing AI Applications in 2026

Now that you know how to test AI systems, these are some tools that’ll help you carry out the process.

CoTester: CoTester is an AI testing agent that helps you create tests for your app from user stories, execute them across real environments, self-heal locators to minimize manual maintenance, and give you live feedback, so you can identify issues and debug quickly. This agent learns continuously from past data and adapts to improve the accuracy of your tests.
QA Wolf: This is an AI testing platform for web and mobile apps that helps you make sure features that use Gen AI return consistent and precise results and understand when your prompt model or agent caused a regression. With QA Wolf, you can perform model, model consistency, and invariance testing.
Mabl: Mabl’s agentic tester works like an intelligent co-pilot and assists you in test creation, execution, analysis, and maintenance. You can plan E2E tests, autonomously triage all failures, and get insights and recommendations directly into your Jira tickets. Mabl interacts with your app like a real user and enables you to design realistic tests.
Testim: Testim lets you automatically build tests using natural language prompts. All you need to do is describe what you want to test, and the custom agent workers take care of the rest. You can run tests based on changes, map tests to changes, identify defects, and speed up root cause analysis with error aggregation and comparison screenshots.
Functionize: Functionize is an AI testing platform that is powered by specialized AI agents who can think, adapt, and act. These intelligent agents can help you create high coverage tests, self-heal to reduce maintenance, keep your team and stakeholders in the loop about execution progress, and diagnose issues early in the development cycle.

Best Practices for Testing AI Applications

1. Design for explainability: Explanability helps you understand the reasoning behind your AI model’s decision. This factor is actually very important for debugging, compliance, and trust because it enables your team and stakeholders to know why the model made a decision. Moreover, transparent audit trails are necessary for meeting regulatory requirements.

Pro tip
You can apply techniques such as feature importance, SHAP, or LIME which will allow you to explain how inputs affect your model’s decisions and improve traceability.
2. Focus on probabilistic validation rather than binary pass/fail: Since AI systems are inherently probabilistic, you cannot just mark outputs as ‘pass’ or ‘fail’. You have to evaluate them using statistical measures, confidence levels, and acceptable performance scores.

Pro tip
You should assess metrics like precision, recall, and confidence scores, and set acceptable thresholds like ‘90% accuracy within a confidence range’ so your testers aren’t confused about what success means.
**3. Collaborate with developers and data scientists: **For comprehensive testing of your AI apps, testers, developers, and data scientists need to work together. Data scientists can assist you in understanding model behavior, assumptions, and limitations. Developers can contribute to reproducing and fixing issues. This collaboration enables you to design better test cases and analyze results accurately.

Pro tip
You can create shared documentation for datasets, model versions, and assumptions to avoid misalignment and quickly trace if an issue came from data, code, or the AI model.

How CoTester Helps Build Trustworthy AI Applications

Many businesses are now building autonomous AI apps to enable their users to complete tasks faster and more efficiently.

But this also means developers have to ensure these AI systems can operate within safe and expected boundaries. And for that, you need AI app testing platforms that let you generate comprehensive reports, maintain audit trails, enforce guardrails, and oversee every action.

CoTester gives you detailed execution logs along with screenshots, asks for your team’s approval before taking an action, allows you to securely parameterize test data, customize how and when tests run, and keeps you in the loop at all times.

So, no matter how complex your AI app is, this agent will help you accelerate testing, ensure consistent performance, and expand coverage, all without compromising control.

This blog is originally published at TestGrid

Audio Testing for Media Apps: Automation Techniques & Best Practices

Irina Kozlova — Thu, 07 May 2026 16:39:42 +0000

Have you ever been in a scenario where your audio features pass every functional test, yet support tickets keep appearing?

Just imagine: audio drops during live sessions, voice responses lag on specific devices, and playback drifts out of sync after minor network fluctuations.

Because media behavior depends on browser engines, codecs, hardware variation, operating system audio handling, and bandwidth stability, even the smallest differences across environments can create inconsistent results.

The worst part? These issues get missed during basic validation.

That’s where audio testing becomes essential. In this blog, we’ll discuss what it does and how you can run automated audio tests across real devices in the cloud.

What Is Audio Testing?

It’s a testing technique that verifies an application’s audio features function reliably across devices, formats, and real-world usage conditions.

This includes validating playback behavior, AV synchronization, device compatibility, and error recovery. Audio testing also evaluates various performance factors such as latency, jitter, packet loss, and synchronization with video streams.

Common Types of Audio Scenarios

Audio testing varies depending on how sound is delivered and processed within your application. Each scenario introduces different failure points and validation requirements:

1. Voice Applications

Here, you test voice assistants, conversational interfaces, and IVR systems. You check speech-to-text/text-to-speech accuracy, intent recognition, and response latency, and well, the system handles multi-turn interactions across different devices, microphones, and acoustic environments.

2. Streaming Audio Delivery

Streaming systems deliver audio through continuous data transfer and buffering logic. You verify playback start time, buffer management, adaptive bitrate switching, and recovery after network disruption. Performance under fluctuating bandwidth becomes a critical factor.

3. File-based Audio Playback

This scenario involves pre-recorded audio files stored locally or on a server, such as media libraries, podcast platforms, and learning modules.

You test format support, codec compatibility, seek accuracy, completion events, and playback controls, including how the system deals with corrupted or unsupported files.

4. Real-time Audio Communication

Applications that support live audio communication often depend on technologies such as WebRTC.

Here, you examine various parameters such as packet loss recovery, echo cancellation, background noise suppression, and transmission latency, especially when audio runs alongside video streams.

Key Areas to Validate During Audio Testing

Audio testing focuses on three core validation areas, each targeting a different class of defects:

1. Network and Performance Stability

Streaming and real-time audio depend on continuous data transfer. Now, under stable bandwidth, playback may appear reliable to you. However, under fluctuating conditions, failures occur rapidly.

Adaptive streaming formats such as HLS or MPEG-DASH allow the player to switch between different bitrate segments based on available bandwidth.

If segment loading thresholds or buffer configurations are misaligned, users experience repeated rebuffering even when bandwidth appears sufficient. It’s also important for end-to-end latency to remain within acceptable thresholds for conversation flow.

Real-time audio systems rely on jitter buffers to smooth these variations, but large fluctuations can still produce gaps, distortion, or delayed playback.

Packet loss can also trigger packet-loss concealment or forward error correction mechanisms, which may introduce distortion or short audio gaps.

2. Audio Quality and Format Handling

Audio playback depends on both the container format and the underlying codec.

Common formats such as MP3, WAV, AAC, and OGG must be tested across devices and browsers, as different format and codec combinations can lead to decoding errors, silent playback failures, or inconsistent audio quality.

For example, a file packaged in an MP4 container may use Advanced Audio Coding (AAC), while another may use Opus or MP3.

Since browser media engines and operating systems don’t support every combination uniformly, a mismatch can result in decoding errors, silent playback failure, or poor output quality.

In addition, bitrate configuration affects consistency.

High-bitrate audio may perform well on stable networks but trigger buffering under constrained bandwidth. Incorrect sample rate handling can introduce distortion or pitch variation, especially when devices resample audio internally.

Channel configuration introduces another risk – stereo, mono, or multi-channel audio must be rendered correctly across speakers, headsets, and Bluetooth devices. Testing these transitions is critical, as operating systems may change audio routing, latency, or output behavior.

3. Playback and Interaction Behavior

Audio features must respond predictably to user actions and system events, such as play, pause, seek, and completion callbacks. If event listeners are misconfigured, the UI state may change while playback fails in the background.

Seek behavior introduces precision challenges. Jumping to a new timestamp requires correct buffer alignment and segment loading. In streaming scenarios, improper segment indexing can cause delayed temporary silence or delayed playback.

System interruptions such as incoming calls, notifications, or app backgrounding add further complexity, as they can pause playback, shift audio focus, or prevent proper resume behavior.

How to Implement Automated Audio Testing

Understanding where audio failures occur is only the first step. To catch them before your users do, you need to execute automated tests that properly measure real playback behavior under controlled conditions.

But before we do that, it helps to understand how traditional manual testing compares with automated approaches. Their goal may be the same, i.e., to validate audio systems. However, the differences become clear when it comes to scale, precision, and repeatability.

Manual vs Automated Audio Testing

Now, follow this practical sequence to implement automated testing for audio:

1. Define Audio Workflows and Success Criteria

Before writing a single test script, define measurable thresholds. Without numbers, your automation efforts will have nothing to assert against.

Therefore, start with the playback start time. For example:

Playback must begin within ≤ 2 seconds after a user clicks “Play” on a standard broadband profile
Under simulated 3G, playback must begin within ≤ 5 seconds
If the action exceeds your threshold, the test fails.

Next, set audio-video sync tolerance for media applications. For example:

Sync deviation must remain within ≤ 100 ms during stable playback
If drift occurs under network fluctuation, it must be corrected within ≤ 500 ms
You can measure this by comparing timestamps between audio and video streams or by tracking divergence in their playback clocks.

Then, fix the buffering tolerance. For example:

Rebuffering frequency: ≤ 1 event per 5 minutes under stable broadband
Total buffer time: ≤ 3% of total playback duration
Your automation should log “waiting” events and calculate cumulative buffer time against total playback duration.

Lastly, for real-time voice systems, determine conversational latency. A practical range could look like this:

End-to-end latency: ≤ 300 ms for natural interaction
Acceptable upper bound under moderate instability: ≤ 800 ms
You can calculate the latency between audio input capture and response playback start.

2. Automate Media Controls and Verify actual Playback

After triggering playback, you can access the “HTMLMediaElement” and assert its state directly. For instance:

Record the timestamp when the “Play” action is triggered
Wait for the “playing” event
Measure the time difference and assert it is within your defined threshold (for example, ≤ 2 seconds under broadband)
After that, validate playback progression by checking that:

“paused” is false
“currentTime” increases steadily over a short interval (for instance, increases by at least 1 second over a 1.5-second observation window)
“readyState” indicates sufficient data for playback
This confirms that your media playback is progressing, not just that the UI toggled. Now, monitor key media events:

“waiting” indicates buffering
“playing” indicates resumed playback
“error” indicates decoding or network failure
For instance, “if a ‘waiting’ event is triggered immediately after ‘playing’ under stable network conditions, your automation should flag potential instability.

3. Determine Buffering and Playback Stability

Here, you should attach listeners for the “waiting” and “playing” events. When “waiting” fires, record the timestamp. This marks the start of a buffering pause. On the other hand, when the next “playing” event fires, record the timestamp again and calculate the buffering duration.

Let’s put this exercise into numbers:

If a 10-minute stream buffers 5 times and accumulates 40 seconds of buffering, the interruption rate is about 6.6%. If this exceeds your defined threshold (for example, ≤ 3%), the test should fail.

Also, verify playback continuity. You should track “currentTime” at fixed intervals. If “currentTime” stops increasing without a corresponding “waiting” event, you may face silent playback freezes caused by decoding issues or event handling.

4. Simulate Network Conditions

To do that, you must first define test network profiles and apply them using the browser DevTools protocol or your cloud testing provider’s network throttling tools.

These tools can reproduce bandwidth limits and latency variations to evaluate playback behavior under different connectivity scenarios.

However, browser-level throttling cannot fully replicate real-world radio network instability or complex packet loss patterns, which is why running tests on real devices and networks remains important.

To make this easier to implement, create a network simulation test matrix like the one below:

By simulating instability deliberately, you can expose audio defects that rarely appear in controlled lab environments.

Validate Real-time and Voice Flows For real-time communication, track the time taken between audio capture and audio playback on the receiving side. Here’s how you can do that:

Emit a known audio tone or phrase at the sender’s side
Detect the same signal at the receiver side
Calculate the time difference
So, your acceptable threshold may be:

≤ 300 ms for natural conversation
≤ 800 ms under moderate network instability If latency exceeds this range, conversations begin to overlap or feel delayed. Next, focus on voice application testing.

If your system includes speech-to-text processing:

Use audio injection to feed pre-recorded audio clips as microphone input instead of relying on manual speech
Compare the transcription output against the expected text
Allow a defined tolerance for minor word variance if your system supports fuzzy matching
Acceptable accuracy thresholds are usually defined using Word Error Rate (WER) or token-level similarity.

Example:

WER ≤ 5–10% depending on the application
Then, measure response timing.

You can record the timestamp when STT processing completes and when text-to-speech playback begins. If your response delay exceeds your defined threshold (for example, ≤ 500 ms after recognition), flag performance degradation.

Lastly, don’t forget to test multi-turn interactions.

You can simulate sequential inputs and verify that conversational context is preserved. For example:

“Schedule a meeting.”
“Tomorrow at 10 AM”
Confirm that the system links the second input to the prior context correctly.

Here is a technically accurate rewrite that keeps your structure but removes claims that TestGrid itself performs audio analysis. It clarifies that TestGrid provides the execution infrastructure, while validation logic lives inside the test framework.

Enable Automated Audio Validation on Real Infrastructure With TestGrid

Once measurable media criteria are defined, such as playback start time, buffering behavior, synchronization drift, or voice interaction latency, your next plan of action should be to execute those checks in environments that reflect real user conditions.

That’s where TestGrid, an AI-powered end-to-end testing platform, can help.

TestGrid enables automation suites to run on a range of devices and browsers. Meaning, you can easily execute tests against:

Native Android and iOS devices
Real device hardware and operating systems
Real browser engines such as Chrome, Safari, Firefox, and Edge
Because the tests run on real infrastructure rather than emulators, you can observe how application behavior varies across operating systems, browser engines, and device types.

TestGrid also supports executing automation using common testing frameworks and integrating test runs into CI/CD pipelines. This allows media-related tests to run automatically as part of your CI/CD pipeline.. That means you can:

Trigger media test suites on every build
Execute tests across multiple devices and browser configurations
Run tests in parallel to reduce validation time
This blog i soriginally published at TestGrid

AI-Powered Regression Testing Helped This Fintech Platform Save 70% Testing Time

Irina Kozlova — Tue, 21 Apr 2026 15:44:31 +0000

A global fintech enterprise operating a digital payments platform relied on its web application to support high-volume financial transactions across multiple regions.

Customers accessed the platform through a browser-based interface to initiate payments, verify transaction status, manage accounts, and review payment histories.

Behind the interface, the application coordinated complex operational workflows including payment authorization, fraud checks, transaction reconciliation, and regulatory validation.

As the platform expanded across markets and new features were introduced, maintaining release velocity became increasingly difficult. The company’s regression testing process began to slow down delivery cycles and delay production updates.

The Challenge

The fintech enterprise’s Quality Engineering team maintained a regression suite covering more than 700 scenarios across the platform’s core workflows, including:

User authentication and secure account access
Payment initiation and authorization
Transaction history validation
Fraud monitoring alerts
Payment confirmation and reconciliation Because financial transactions require strict accuracy and regulatory compliance, every release required thorough validation of these workflows.

Regression testing typically took around eight days to complete, combining automated tests with manual verification of complex payment scenarios.

Even minor platform updates triggered full regression cycles to confirm that transaction processing, authorization responses, and account management behaviors remained stable.

This created several operational challenges:

Releases were delayed while regression validation completed
Automation maintenance consumed significant engineering time
New features often waited for the next release window due to testing delays
QA engineers spent time repairing automation before executing regression tests As the platform continued to grow, the regression suite expanded faster than the team could efficiently maintain it.

Why Existing Approaches Fell Short

The team relied on a combination of manual testing and traditional automation frameworks to validate payment workflows.

Automation covered many critical scenarios, but maintaining those scripts required continuous engineering effort. Small interface updates frequently broke locators, forcing engineers to repair test scripts before regression runs could begin.

At the same time, many financial workflows still required manual validation. QA testers simulated real payment flows to confirm authorization responses, transaction record generation, OTP confirmations, and receipt creation.

The CoTester Approach

To improve regression efficiency, the fintech enterprise adopted CoTester, an AI testing agent designed to automate and stabilize testing for modern web applications.

The rollout began with a focused pilot targeting the platform’s most critical payment workflows, including payment authorization, transaction confirmation, and secure account access.

During the initial implementation phase, the Quality Engineering team integrated CoTester into their testing workflow over a four-week configuration period. Existing regression scenarios from Jira were imported into the system, and AI-generated tests were reviewed and refined by QA engineers to ensure they accurately represented real payment flows.

Once validated, CoTester was integrated into the team’s testing process and expanded to support broader regression coverage across the platform.

Key improvements included:

1. AI-generated test creation

CoTester converted product requirements and user stories into structured test cases covering authentication flows, payment authorization, and account management operations. This enabled the team to expand regression coverage without manually scripting new tests.

2. Vision-based UI understanding

Instead of relying only on brittle selectors, CoTester interpreted page structure and visual context when interacting with application screens. This reduced failures caused by minor interface updates.

3. Self-healing test execution

During execution, CoTester automatically adapted to UI changes by adjusting element detection and interaction logic. Broken locators no longer required immediate manual fixes before regression runs.

4. Parallel regression execution

Regression suites executed simultaneously across multiple browser environments, significantly reducing the time required to validate payment workflows.

5. CI-triggered validation runs

The team configured CoTester to execute regression tests during nightly builds and key release checkpoints, enabling continuous validation as the platform evolved.

While test automation coverage expanded significantly, some scenarios — particularly those involving dynamic fraud-detection rules — continued to require manual review. However, the overall regression process became far more stable and predictable.

The Impact

Within the first few release cycles using CoTester, the fintech enterprise reported measurable improvements in regression efficiency.

Regression cycle time was measured as the wall-clock time required to complete the full regression suite before each release. Baseline measurements were taken from the three release cycles preceding CoTester adoption. Post-implementation measurements were averaged across the following three releases.

The results included:

70% reduction in regression cycle time: Validation decreased from an average of eight days to under three days
Faster defect detection: Issues affecting payment flows surfaced earlier during development
Reduced automation maintenance: Self-healing execution lowered time spent repairing scripts
Expanded regression coverage: Additional authentication and payment scenarios were validated without increasing manual effort
Shorter regression cycles allowed the enterprise to move toward more frequent release cadences while maintaining confidence in transaction reliability and regulatory compliance.

What Changed for the Quality Engineering Team

Testing operations shifted from maintaining automation scripts to focusing on validation strategy and test coverage.

Instead of spending time diagnosing broken locators or coordinating lengthy regression runs, engineers focused on reviewing AI-generated tests and analyzing execution results.

Regression testing evolved from a periodic release activity into a continuous validation process integrated into the development workflow.

The team also gained clearer visibility into test outcomes and recurring failure patterns, allowing them to prioritize improvements across the payment platform.

What the Quality Engineering Team Had to Say

“Before CoTester, every regression run started with figuring out which automation scripts were broken after the latest UI updates. Sometimes we spent the first day of regression just repairing tests before we could even validate payment flows. Once CoTester was in place, those locator failures dropped significantly and the regression run itself became much more predictable.”

— Director of Quality Engineering, Global Fintech Platform

See How CoTester Accelerates Regression Testing
For fintech teams operating digital payment platforms, every release must validate critical transaction workflows without slowing delivery.

CoTester enables teams to generate, execute, and maintain regression tests using AI testing agents that adapt to application changes and keep validation aligned with evolving product requirements.

This blog is originally published at TestGrid

How Continuous Testing Improves Salesforce ERP Performance in 2026

Irina Kozlova — Wed, 01 Apr 2026 09:56:07 +0000

A 2025 report shows that the Salesforce service market is expected to grow from $19.9 billion in 2025 to a whopping $84.7 billion by 2035. Another study by the IBM Institute for Business Value found that 61% of data pioneers say Salesforce helped them achieve faster time-to-market.

Do you know why businesses prefer Salesforce-based ERP environments?

Because they can build highly customizable workflows, adapt business processes without overhauling core ERP systems, and build intuitive interfaces that enable sales, finance, and operations teams to work efficiently within the same platform.

But this same flexibility also complicates your testing process. Why? This is what we will discuss in this blog.

What Makes Testing Salesforce-Based Environments Tricky?

1. Frequent Updates: Salesforce pushes three major releases every year–Spring, Summer, and Winter. Each of these releases typically includes new features, UI changes, security and permission updates, and performance improvements.

The updates can interfere with your existing custom workflows, and:

Alter APIs or deprecate features that integrations and custom code depend on
Modify security settings that can restrict user access
Change standard object behavior that impacts downstream ERP processes
2. Deep Customization: A big reason why most organizations go for Salesforce is that it lets you customize your workflows as you want and align them with specific business processes.

However, because workflows are layered across Apex, Flows, validation rules, and triggers, a metadata change in one object can cascade across dependent transactions. This is why your testing also has to account for these interdependencies, not just standalone features.

3. Dynamic UI Components: Salesforce doesn’t have a static user interface. Its component-based dynamic UI (Lightning) uses Shadow DOM and challenging element IDs, which often break traditional test scripts.

Moreover, Lightning components change behavior based on user roles, permissions, data context, and device type. The same screen can behave very differently for different users.

For example, in an “Order Record” page, a sales rep can view order details, edit quantity, and update delivery status. While a finance manager might see pricing adjustments, tax fields, and approval actions.

This dynamicity of Salesforce makes it tough to design stable and reusable tests. Role-based rendering in Lightning means transactional workflows can behave differently depending on profile, field-level security, and record ownership.

4. Bulk Operations and Concurrency: ERP processes usually involve mass order updates, invoice generation, data syncs, or large volumes of data updates. Concurrency and large-volume operations can expose governor limits, record locking conflicts, and partial transaction failures.

The problem is, these issues don’t surface in single-record tests. You can only detect them if you test with real-world load conditions.

5. Integration Issues: Most Salesforce environments integrate with third-party services such as payment gateways, inventory systems, accounting tools, and external data sources. Each of these integrations relies on APIs, middleware, and scheduled jobs, which aren’t in your control.

Failures often appear only after asynchronous jobs complete, when downstream systems reject or partially process transactions.

Continuous Testing Strategy for Salesforce ERP Environments

Continuous validation in Salesforce ERP environments requires structured controls across deployment, workflow design, and system monitoring.

Here’s what to do:

1. CI/CD-driven Validation

Validation should trigger automatically whenever Apex code, metadata, flows, validation rules, or permission models change. Continuous execution across regression, API, and performance layers reduces the risk of introducing instability during releases.

2. Early Workflow Review

Validation should begin at the design stage. Reviewing object relationships, approval logic, and integration touchpoints before deployment reduces downstream rework and prevents transactional errors from reaching production.

3. Risk-based Workflow Prioritization

Revenue-impacting processes such as order processing, invoicing, payments, inventory updates, and approvals should be validated on every release cycle. Test coverage should reflect business impact, not just feature count.

4. Environment Consistency Controls

Salesforce sandboxes, scratch orgs, and production environments must remain aligned. Differences in metadata, scheduled jobs, or masked data can produce production-only failures if not continuously monitored.

5. Stability and Impact Monitoring

Monitoring should track workflow stability trends, defect escape rates, and the impact of seasonal Salesforce releases. Continuous insight into validation coverage enables informed release decisions.

What are the Types of Tests You Must Cover in Continuous Testing?

1. Business Workflow Testing

Functional tests that focus on verifying UI components aren’t enough for Salesforce ERP environments. You also need to validate complete business workflows. This typically includes:

Order-to-cash processes
Invoice generation and approvals
Payment updates
Inventory adjustments
Role-based approvals The main goal is to ensure that changes don’t break multi-step transactions that affect revenue or financial reporting.

2. Regression Testing across Customizations

Salesforce ERP systems mainly depend on Apex code, Flows, validation rules, custom objects, and managed packages. Therefore, your regression testing must confirm that updates to metadata or configurations do not disrupt your existing workflows or integrations. And ensuring this becomes even more critical during seasonal Salesforce releases.

3. Integration Testing across Systems

Salesforce ERP doesn’t always operate in isolation. It connects with various services such as accounting systems, payment gateways, marketing tools, inventory platforms, and middleware. Production failures often originate from integration gaps between these services rather than UI errors.

With Salesforce system integration testing, you can ensure:

Data flows correctly between systems
Status updates stay consistent
Error handling works as expected
No duplicate or partial transactions occur

4. Bulk and Performance Testing

Most ERP operations involve high-volume transactions. Bulk updates, approvals, batch jobs, and concurrent users can expose limits in Apex and Flow execution. Performance testing helps you evaluate batch processing stability, governor limit thresholds, record locking behavior, and system behavior under peak transaction loads.

This way, you can prevent production slowdowns during critical business periods.

5. Security and role-based Validation

Salesforce ERP environments frequently share, access, and manage sensitive financial and operational data.

Therefore, your testing must confirm:

Correct profile and permission behavior
Field-level security enforcement
Approval controls
Proper access segregation between teams

Role variance is one of the most common causes of production-only issues.

How CoTester Enables Continuous Testing for Salesforce

CoTester is an AI-powered software testing agent built to continuously validate your Salesforce changes. It transforms real business workflows, user stories, and production deployments into executable, self-maintaining automated tests so every release is verified against how your users actually work.

Continuous testing for Salesforce ERP environments needs more than scripted UI automation. You need traceable validation tied to requirements, stable execution across seasonal releases, and governance controls that match enterprise risk standards.

CoTester is designed to support this model by anchoring test execution to approved Salesforce changes and business workflows.

With the agent, you can:

Upload Salesforce user stories, change tickets, or configuration updates, and CoTester automatically turns them into structured test definitions that reflect actual business intent across objects, roles, and approval chains
Keep your tests linked to their originating requirement and maintain traceability from change request to execution outcome
Execute tests in real browser environments and validate object-level permissions, conditional field visibility, approval transitions, record state changes, and cross-object updates
Frequent Salesforce releases can result in UI adjustments, layout updates, and metadata changes. CoTester uses vision-language context during execution to resolve UI elements based on structure and intent rather than depending on brittle locators.

This helps you reduce maintenance overhead during Spring, Summer, and Winter releases without detaching your tests from their original business logic.

Moreover, continuous testing requires deterministic triggers. CoTester seamlessly integrates with multiple CI/CD tools, including Jenkins, GitHub Actions, and Azure DevOps, to enable CI/CD testing for Salesforce and execute ERP validation suites whenever:

Apex code changes
Metadata updates are deployed
Integration configurations shift
Release branches are merged This blog is originally published at TestGrid

AI-Powered Regression Testing for Scalable and Agile Quality Engineering

Irina Kozlova — Tue, 03 Mar 2026 18:17:22 +0000

You add new features, updates, and UI enhancements to your app to improve user experience and stay competitive. That’s a given.

But what’s frustrating is when one small change affects multiple features and workflows. Suddenly, something that was working fine starts causing errors. What’s even more exhausting is having to update dozens of tests just to keep up.

Modern apps have numerous features, each supported by several tests. Manually maintaining them is just not practical.

This is why integrating AI into your regression testing workflow is critical.

What is AI regression testing? How is it different from traditional testing, and what are the steps to implement it? We’ll cover all this and more in this blog.

Accelerate regression cycles, minimize flaky tests, and reduce tiring maintenance with CoTester.

TL;DR

AI regression testing is the use of artificial intelligence to automate test creation, execution, prioritization, and analysis for faster defect detection and releases

Traditional regression testing struggles with growing test suites, flaky tests, and rising maintenance overhead

AI tools leverage machine learning, computer vision, NLP, and predictive analytics to reduce testing time, minimize test maintenance, and enable faster feedback

AI regression testing enhances defect detection, accelerates root cause analysis, enables intelligent change impact analysis, improves visual regression validation, and detects anomalies in performance

To adopt AI regression testing, start with requirement analysis, then select the right testing tool, prepare your test data and environment, integrate with CI/CD, pilot on low-risk modules, and refine AI model outputs

Challenges in Modern Regression Testing

Apps are a lot more complex today. Faster release cycles, microservices-based architectures, API dependencies, frequent UI updates, and a growing number of test suites can make regression testing taxing.

Continuous delivery increases the number of regression runs you need
Large test suites can make maintenance difficult if you have to manually update scripts for every change
Without smart test selection, executing the entire test suite for even minor changes can slow down delivery If your team has to constantly fix brittle tests and maintain huge test suites, it only increases the risk of missing defects, reduces confidence in regression coverage, and diverts focus away from testing critical features.

What Is AI Regression Testing?

Traditional regression testing requires you to manually re-execute test cases to ensure recent code changes haven’t introduced unintended issues in your app’s existing features. But this repetitive nature can take up a lot of time and resources, increase the chance of human errors, and leave coverage gaps.

AI in regression testing means incorporating artificial intelligence components like machine learning (ML) models, computer vision, and anomaly detectors directly into the regression testing workflow for more efficient and adaptive automation.

ML models analyze patterns in code changes and historical test outcomes to predict and prioritize critical areas
Computer vision helps you compare UI screenshots to catch visual regressions
Anomaly detectors can learn what “normal behavior” is from past logs and telemetry to flag unusual deviations during regression tests
Self-healing agents automatically adapt your tests to UI element locator changes and reduce test failures AI can also help you with intelligent test generation, automatic regression test execution, and smart test selection so you can run tests based on impact, minimize testing time, and optimize accuracy.

Why Do You Need AI for Regression Testing?

Reduced test execution time: AI testing allows you to run only the tests that matter. Intelligent test impact analysis and change-based selection use code diffs and past failures to identify and prioritize relevant test cases. This means you don’t have to execute the entire test suite for every change. You can cut needless runs and minimize the total testing time, save compute, and speed up feedback loops.
Lower maintenance cost: Traditional testing needs constant human intervention to update test scripts after code changes. But machine learning models can automatically adapt to the UI changes, locator adjustments, and workflow shifts, and update your tests accordingly. This helps you almost eliminate manual script fixes and minimize the maintenance burden.
Faster feedback in CI/CD: When you integrate an AI-driven regression testing tool in your CI/CD pipelines, it can detect the changes you’ve made, execute tests, and give you prompt feedback on issues. This way, you can easily identify and resolve bugs immediately and accelerate your release velocity.
Better UI/UX protection: Computer vision and smart visual AI analysis can efficiently spot even minor layout, alignment, or styling issues. Rather than pixel-by-pixel baseline comparisons, AI can understand context and highlight changes that actually impact user experience. This helps you reduce false positives and ensure visual integrity across devices and screen sizes.

Difference Between Traditional and AI-Powered Regression Testing

To understand what value exactly AI adds to regression testing, you need to know the gaps in traditional regression testing and what improvements AI delivers.

How AI Enhances Regression Testing, Improves Test Stability, and Reduces Flakiness

1. Efficient defect detection: Machine learning models evaluate defect reports, test results, code changes, and production incidents to identify defects caused by code updates. This way, you can detect even the subtle issues that human testers might miss and resolve them before they reach your users.

2. Predictive analytics to run the right tests: ML models are usually trained on data related to test failure history, past test outcomes, and code churn. And based on this data, the models predict potential areas in your app impacted by the changes, so you can focus your regression testing efforts on those areas. You can speed up your testing cycle when you eliminate executing unnecessary tests.

3. Faster root cause analysis: AI can help you automatically process and correlate large volumes of test logs, error traces, telemetry, and environment signals to pinpoint why a regression test failed. You can easily trace back failures to their root cause, with very little human intervention required.

4. Change impact analysis for smart testing: AI testing tools examine code modifications and track which features and tests these changes affected. They map dependencies and change contexts to focus testing where risk is highest. With each update, you can ensure that all risky and critical areas of your app are thoroughly validated.

5. AI visual regression testing: Many AI testing tools leverage components like computer vision, deep learning, and optical character recognition (OCR) to find visual changes in your app’s UI that traditional pixel comparison may not be able to spot.

AI-driven visual regression testing can recognize buttons, text, layout shifts, and screen patterns to filter out acceptable variations like animations and responsive behavior, and flag UI/UX regressions that affect users.

6. Effective anomaly detection: With the help of machine learning, you can uncover rare edge case failures, unexpected app behavior, or failure rate spikes based on test and performance metrics. This allows you to notice performance degradation and isolate unstable components that cause anomalies early in the pipeline.

How to Implement AI in Regression Testing

These are the typical steps you can follow for AI implementation in regression testing.

1. Perform requirement analysis: First, assess your business goals, app complexity, data availability, and existing automation stack. Now, understand where AI can add value. It can be flaky test detection, visual validation, or defect analysis.

Try to focus on the areas with repetitive execution and high test failures.

2. Select the right tool: The efficiency, scalability, and accuracy of your regression testing depend largely on the quality of the testing tool. Make sure the tool you choose supports:

Self-healing automation so your tests don’t fail unnecessarily after each code change
Smooth CI/CD integration, which will help automate test triggers and get fast feedback
Strong privacy and governance controls to protect sensitive test data and ensure compliance
Intuitive UI so DevOps and quality assurance teams can start testing right away without spending too much time on training 3. Prepare your test data, environment, and pipeline: Make sure your test data is realistic so you can validate your app’s functionality and uncover edge cases under real-world conditions.

Also, ensure the test environment closely resembles production. It should include accurate infrastructure configurations, network settings, third-party integrations, and database versions. Then integrate the AI tool into your CI/CD pipelines to enable test automation.

4. Pilot on low-risk changes: Now, once everything is configured, start testing with low-risk modules and features that have minimal effect on user experience, such as cosmetic UI settings or optional filters. Check test performance, monitor false positives, and scale across critical workflows once you’re confident with the results.

5. Train the model: The more you train the AI tool or agent, the better it will learn and improve prediction accuracy. For this, use information from high-quality historical test results, defect data, code change frequency, and usage patterns.

And human oversight is important to ensure every AI response and action is transparent, explainable, and meets compliance standards.

Another important thing to consider when training AI models is to include automated regression testing for AI prompts. This will allow you to find output regressions, quality drops, hallucinations, formatting errors, or behavioral shifts.

Enable Intelligent Regression Testing With CoTester

CoTester is an AI software testing agent that optimizes regression testing by automatically generating tests, executing them on real devices and browsers, flagging bugs instantly during execution, and providing live feedback, execution logs, and screenshots.

With CoTester, you stay in complete control of test execution. You can edit steps, adjust scripts, and guide the agent whenever needed. And whether you prefer no-code, low-code, or direct scripting, the agent offers you flexibility without locking you into a single approach. This makes CoTester one of the leading AI tools for regression testing.

CoTester seamlessly adapts to your QA workflows and creates tests from your Jira stories
It integrates with GitHub Actions, Jenkins, and Azure DevOps to execute tests automatically after app changes
AgentRx, an auto-heal engine, detects UI modifications, layout shifts, and even complete redesigns to update your tests dynamically
CoTester leverages a multi-modal Vision Language Model (VLM) to interpret your app’s screen, including visuals and text, like a human tester, to make more reliable test decisions
You can easily schedule test execution as per your requirements, whether it’s before a major release or your weekly regression runs
CoTester learns from learns from tests and feedback to adapt and reduce test flakiness
You can also leverage the Test Scheduler Agent to trigger regression tests after code merges, deployment, and based on environment availability and compute capacity
Orchestrate test execution based on code commits, reduce regression testing time, and ship faster with CoTester

This blog is originally published at Testgrid

Ecommerce Performance Testing Checklist: What to Test Before You Scale

Irina Kozlova — Mon, 23 Feb 2026 18:13:53 +0000

In eCommerce, even a few milliseconds can make a big difference. A slight delay in page loads, or a short pause during checkout, can force your users to abandon their purchase and move on to a competitor.

This risk increases even more in events like flash sales, product launches, or festive sales. And when performance degrades, it directly impacts revenue, customer trust, and brand image.

That’s why you must ensure your eCommerce site performs well under stress, whichmeans your pages load quickly, searches return results without lag, carts update instantly, and checkouts complete efficiently when thousands of users are active at the same time.

But how do you achieve all these? Through performance testing.

In this blog, we will discuss the different types of eCommerce performance testing, how to execute them, what metrics to measure, and the best practices to follow, along with some examples of performance testing scenarios for eCommerce websites.

What is Ecommerce Performance Testing?

Ecommerce performance testing is a type of non-functional testing that helps you evaluate how your website or app performs under different user loads, particularly when traffic spikes or gradually increases.

The aim here is to check the stability, responsiveness, speed, and scalability of critical features or workflows, so you can give your users a smooth shopping experience.

Performance tests should typically answer these questions:

How does the site behave during traffic surges?
Which user journeys degrade first under load?
Are response times within acceptable limits for users?
Is the performance consistent across devices and geographies?

Why Ecommerce Performance Testing Isn’t Optional

Yottaa’s Web Performance Index shows that pages that take longer than 4 seconds to load experience bounce rates of 63%. This means nearly two-thirds of the visitors leave before engaging with your site further.

Performance issues can cost you potential customers or drive your existing customers away, leading to revenue loss and negative brand perception.

Here’s why performance testing is a non-negotiable:

Smooth UX: Ensure fast page loads, responsive interactions, and seamless navigation, and prevent lags or freezes that hamper shopping experiences

Reliable experience across devices: Verify if the site functions consistently on different mobiles, tablets, desktops, and browsers

Increased conversion rates: Grow your add to cart rates and complete purchases by ensuring faster product discovery, quick cart updates, and stable checkouts

Improved customer loyalty: Deliver steady performance so your users repeat visits and purchases

Fewer payment failures and cart drop-offs: Test peak load and third-party dependencies to reduce checkout slowdowns, payment errors, and abandoned carts

Key User Journeys that Define Your Ecommerce Site Performance

These are some of the most important user journeys in your eCommerce site that you must test to ensure your users can browse and buy without delays.

1. Product Search

Product search is usually the first critical interaction your users will have with your site after login. So they expect it to be fast and give them relevant results with filters, sorting, and suggestions responding immediately. If the search function slows down, your users might think the product isn’t available and leave the site.

What to test

Search response time under concurrent users
Throughput under query-heavy search traffic
Filter, sort, and pagination latency

2. Add to Cart

Your add to cart flow plays a big part in whether a user decides to make a purchase. Even if a product is readily available, inefficient cart updates can drive away a potential customer. Users expect the cart to reflect accurate pricing, update the right quantities, and stay consistent across sessions.

What to test

Inventory locking and pricing validation latency
Backend API and dependency response times
Failure handling and retries

3. Checkout Flow

In checkout performance testing eCommerce environments, multiple checkout requests at once may cause issues like state loss, backend contention, or dependency failures, which can further lead to retries, errors, and incomplete purchases. Your users expect a smooth, predictable experience where address entry, shipping selection, payment, and order confirmation happen without interruption.

What to test

Order placement throughput
Session stability and state persistence
Failure recovery behavior in case of timeouts, retries, or partial submissions

4. Payment Processing

Payment gateway performance testing is one of the most sensitive stages in the purchase flow. Users want fast authorization, clear feedback, and the confidence that their payment is handled securely. Payment gateway integration may encounter issues like callback delays, duplicate requests, or webhook failures.

What to test

Authorization success rate under peak payment bursts
Payment gateway and third-party service latency
Downstream impact of slow payment confirmation

Types of Performance Testing in eCommerce

Load testing: In load testing for eCommerce websites, you simulate expected peak usage and check how your site processes the user traffic and transactions without slowing down or crashing. Here, you mainly observe response consistency, resource utilization, and site behavior under usual load.
Stress Testing: Stress testing helps you push your website to its breaking point by gradually increasing the number of users until it fails. The goal is to basically assess how many users your site can support and how long it takes to recover after downtime.
Spike Testing: Here, you evaluate your site’s stability under sudden traffic surges or extreme load increases to confirm that features stay functional. This helps you understand if features and workflows show severe performance degradation during abrupt traffic changes.
Scalability Testing: Scalability testing allows you to analyze whether your eCommerce site can grow efficiently with rising demand. Here, you don’t test under a fixed load. You test how the site responds when traffic, data volume, and transactions scale up or down as sales events, product catalogs, and order volumes increase or decrease.

How to do Performance Testing for Ecommerce Websites

Before execution begins, you need a structured ecommerce performance testing process that aligns testing with real business workflows and peak traffic behavior.

1. Identify Critical Business Journeys and Traffic Patterns

First, know which user journeys are critical. This will mostly include the flows covering product search to final order confirmation. And recognize when these flows are expected to receive maximum traffic, whether it’s holiday sales, special promotions, or a new product launch.

2. Define Performance Benchmarks

Determine clearly what represents an acceptable user experience and site behavior. Set target thresholds for page load times, error rates, API response percentile, and checkout success rates. Make sure your benchmarks consider business goals, historical data, and user expectations.

3. Design Realistic Test Scenarios

Create test scenarios that include realistic elements like:
Page navigation and user interactions such as clicks, scrolls, swipes, taps, and hover
Various search queries that include common keywords, misspelled terms, and filters
Sort and filter options on product listings, categories, and menus
Add to favorites, different payment options, and discount applications

4. Execute Tests and Analyze Results

Make sure all the critical parts of your site are tested thoroughly, including the product pages, order placement, and payment confirmation. Evaluate your test results and detect queries that take too long to execute, excessive computational power consumption, connectivity issues, failed transactions, and latency spikes.

5. Monitor and Optimize

As your code and infrastructure changes, monitor your site to spot performance regressions. Use these insights to adjust load models, add test scenarios for newly added features, and retest fixes. Continuous monitoring will help you ensure your site stays in top shape as features, users, and traffic grow.

Ecommerce Website Performance Testing Examples

These are some examples of important features and scenarios to help you understand how to design and evaluate performance test cases for eCommerce websites.

Metrics to Monitor for Ecommerce Site Performance

Tracking the right ecommerce performance testing metrics helps you understand how infrastructure, APIs, and user-facing components behave under load.

Although there can be numerous ecommerce performance testing metrics to measure website performance, these are some of the most important ones.

Challenges in Performance Testing for Ecommerce

1. Simulating High Traffic Volume

Mimicking realistic traffic patterns is not just about the number of users. You also have to account for critical user paths, geographic distribution, network volatility, and session behavior. Poorly modeled traffic fails to uncover real performance issues like latency spikes, resource exhaustion, and database slowdowns.

Best practice
You can use production analytics to shape real load patterns, including arrival rates, peak bursts, and flow mix covering search, cart, checkout, and payment. This can help you spot performance issues that only happen under real usage.

2. Testing Dynamic Content

Content on eCommerce websites is often dynamic in nature, such as personalized recommendations, offers, inventory status, and promotions. And this can be tough to test because responses can vary per user.

Best practice
Design your tests with diverse user profiles, varying cart states, and personalized datasets, and monitor the backend services that are responsible for personalization, pricing, and inventory to ensure content can consistently update even under concurrent traffic.

3. Validating Third-Party Services and Integrations

Integrations with payment gateways, tax engines, shipping providers, and analytics tools are common for eCommerce sites. But this also makes testing difficult because these integrations are outside your control, have their own rate limits, and may behave unpredictably.

Best practice
Replicate third-party behavior via service virtualization and assess how retries, delays, or outages in external services affect the overall performance of your website.

How can TestGrid help in Ecommerce Performance Testing

TestGrid is an AI-powered software testing platform that eliminates complex setup, allows you to automate load, scalability, and stress testing for eCommerce applications, and gives you real-time insights across real devices.

You can also easily track CPU, memory, network usage, and UI responsiveness during live test sessions. The platform’s real browser testing helps you assess your website’s speed, responsiveness, and stability under real conditions and across Safari, Chrome, Firefox, Opera, and Samsung Internet.

Here are some of TestGrid’s best features that make it one of the best tools for eCommerce performance testing:

Assess your site’s performance under varying battery life, network conditions, and swipe gestures
Prevent errors before they reach production and minimize the Mean Time to Resolution (MTTR) with quick alerts and faster debugging
Deploy in public cloud or private cloud and run cloud load testing at scale
Integrate TestGrid with your CI/CD tools like Jenkins, CircleCI, and Azure, and ensure fast delivery cycles
This blog is originally published at TestGrid

TestRigor vs Selenium: A Complete Comparison for Modern Testers

Irina Kozlova — Tue, 17 Feb 2026 17:27:40 +0000

Choosing the right automation tool is no longer just a technical decision — it directly impacts release speed, test coverage, and long-term maintenance effort. That’s why the comparison of TestRigor Vs Selenium has become an important topic for QA teams, automation engineers, and decision-makers looking to modernize their testing strategy.

Both tools aim to solve the same problem — efficient test automation — but they follow very different approaches. Selenium has been the industry standard for years, offering deep flexibility and strong community support. TestRigor, in contrast, represents a new generation of AI-driven automation tools focused on reducing coding effort and simplifying test creation.

Understanding the strengths, limitations, and ideal use cases in the TestRigor Vs Selenium discussion helps teams avoid costly tool mismatches. The right choice depends on factors such as team skillset, application complexity, maintenance expectations, and the level of scalability required.

This guide provides a detailed breakdown of TestRigor Vs Selenium and also helps you identify alternatives to TestRigor for modern testing needs.

TestRigor Vs Selenium – Overview

When looking at TestRigor Vs Selenium, the main difference lies in how each tool approaches automation. Both are used to automate testing, but their design philosophy and technical foundations are completely different.

Selenium is a traditional, open-source automation framework built primarily for web application testing. It gives teams full control over their automation framework, making it highly customizable and extensible. However, this flexibility comes with complexity, as teams must design, build, and maintain their own automation structure.

TestRigor represents a modern shift in automation. Instead of focusing heavily on coding, it emphasizes AI-driven, plain-English test creation. This makes automation more accessible to non-programmers while reducing maintenance overhead. In the comparison, this difference in approach plays a major role in tool selection.

Another key contrast in TestRigor Vs Selenium is platform scope. Selenium mainly targets web applications, whereas TestRigor supports web, mobile, desktop, APIs, and real-world workflows such as email, SMS, and 2FA validations. This broader coverage makes TestRigor attractive for teams seeking unified testing.

In short, the discussion is not about which tool is more powerful — it’s about which solution, including a testrigor alternatives, aligns better with your team’s technical skills, project scale, and maintenance expectations.

What is Selenium?

Selenium is an open-source automation testing framework primarily used for web application testing. It has been a dominant force in the automation space for over a decade and is trusted by startups as well as large enterprises. Its biggest strength lies in flexibility — teams can design automation frameworks exactly the way they want.

Selenium supports multiple programming languages such as Java, Python, C#, JavaScript, and Ruby. It also enables cross-browser testing across Chrome, Firefox, Edge, and Safari, which makes it a strong choice for web applications that must work consistently in different environments.
The most widely used component is Selenium WebDriver, which directly interacts with browsers and simulates real user actions like clicking, typing, and navigating. This allows teams to automate end-to-end user workflows.

However, in the TestRigor Vs Selenium comparison, Selenium is often seen as more technical. It requires coding skills, framework setup, integration of reporting tools, logging mechanisms, and maintenance effort. While powerful, this can increase the learning curve and long-term overhead for teams without strong automation expertise.
Selenium remains a solid choice for projects that demand high customization, complex logic handling, and complete control over the automation architecture.

What is TestRigor?

TestRigor is a modern test automation platform built around AI and natural-language-based testing. Instead of relying heavily on programming languages and technical framework design, TestRigor allows users to create test cases using plain English instructions. This makes automation more accessible to manual testers, business analysts, and product teams.

One of the major highlights in the TestRigor Vs Selenium discussion is TestRigor’s reduced maintenance effort. The platform uses AI to understand UI changes and adapt tests accordingly, which helps minimize test failures caused by small interface updates.

TestRigor is not limited to web testing. It supports web, mobile, desktop, and API testing from a single platform. In addition, it can handle real-world workflows such as email validation, SMS verification, phone call flows, and two-factor authentication scenarios. These capabilities make it stand out in broader test coverage areas.

Because TestRigor is delivered as a SaaS solution, teams don’t need to build a framework from scratch. Built-in reporting, dashboards, and cloud execution further simplify the setup process. In this context, this makes TestRigor appealing for teams that want faster onboarding and lower technical barriers.

TestRigor is especially suitable for organizations looking to scale automation quickly without investing heavily in coding expertise.

TestRigor Vs Selenium – Key Differences

The comparison of TestRigor Vs Selenium highlights major differences in architecture, usability, scalability, and maintenance. While both tools serve automation needs, their approach, technical dependency, and ecosystem vary significantly.

TestRigor is built as a modern AI-powered automation platform that focuses on reducing scripting effort and simplifying automation. Selenium, in contrast, is a code-driven open-source framework that offers flexibility but demands strong technical expertise. Below is a detailed breakdown.

Cost & Licensing

Selenium is completely open-source under the Apache 2.0 license. There are no direct licensing costs, making it attractive for organizations with budget constraints. However, hidden costs often arise in framework setup, maintenance, infrastructure, and skilled automation engineers.

TestRigor operates on a commercial model. While it involves licensing fees, it reduces long-term engineering effort through AI-driven automation and built-in infrastructure, which can lower operational overhead.
Tools like Testsigma position themselves as cost-efficient alternatives by combining modern automation with reduced maintenance effort.

Installation and Setup

In the TestRigor Vs Selenium setup comparison, Selenium requires significant initial effort. It only provides browser automation libraries. Teams must integrate reporting tools, logging mechanisms, assertion libraries, test runners, and CI/CD configurations on their own. Over time, these integrations can increase framework complexity.
TestRigor is framework-ready and SaaS-based. There is no heavy installation or infrastructure setup required. Built-in reporting, dashboards, and execution environments make onboarding faster and simpler.

No-Code vs Script-Based Automation

Selenium is fully script-based. Test cases must be written in languages such as Java, Python, C#, or JavaScript. This requires skilled engineers and limits participation from non-technical testers.
TestRigor uses a no-code, plain-English approach. Test steps are written in human-readable format, allowing manual testers and business users to contribute to automation, accelerating coverage.

Learning Curve

Selenium demands programming expertise, framework design knowledge, and understanding of third-party integrations. For beginners, it can take months to gain proficiency.
TestRigor significantly lowers the entry barrier. Users primarily learn tool usage and application behavior rather than coding syntax.

Integration Capabilities

Selenium is known for its extensibility. It integrates with almost every CI/CD tool, reporting solution, and defect tracking system available. However, these integrations often require coding and framework-level configuration.
TestRigor offers integrations through simplified, visual-based configurations. While the ecosystem may be smaller compared to Selenium, the integration process is faster and less technical.

Mobile Testing Support

Selenium is primarily built for web testing. Mobile automation is possible only through integration with Appium, which adds complexity.
TestRigor supports web, mobile web, and native mobile applications directly, reducing dependency on additional frameworks.

Cloud Testing

Selenium does not provide built-in cloud testing. Teams must configure third-party cloud platforms such as BrowserStack or Sauce Labs.
TestRigor offers native cloud execution with minimal setup, simplifying large-scale test execution.

Documentation and Resources

Selenium benefits from a vast community and extensive documentation built over many years.
TestRigor’s documentation ecosystem is smaller but focused on simplifying onboarding through guided resources.

AI Capabilities

Selenium relies on traditional automation techniques without AI-based test generation or self-healing capabilities.
TestRigor leverages AI and ML for intelligent test creation, element recognition, and reduced maintenance.

Salesforce Automation Support

Selenium does not provide application-specific automation support out of the box.
TestRigor includes dedicated capabilities for Salesforce automation, making it easier for teams working with CRM workflows.

Additional Features

Selenium stands out for customization, advanced integrations, and handling complex edge-case scenarios.
TestRigor differentiates itself with features like SMS testing, email validation, phone call flows, 2FA testing, accessibility validation, and compliance with standards such as SOC2 and HIPAA.

TestRigor and Selenium – Similarities Between Them

While much of the discussion around TestRigor Vs Selenium focuses on differences, both tools share several core similarities that make them strong contenders in the automation space.

First, both TestRigor and Selenium are designed to automate testing and improve software quality through faster and more reliable test execution. They help teams reduce manual effort, increase regression coverage, and accelerate release cycles.

Another similarity is cross-browser testing capability. Both tools allow web applications to be tested across different browsers and environments, ensuring a consistent user experience.

CI/CD integration is also a common strength. Both platforms can be connected with modern DevOps pipelines, allowing automated test execution as part of continuous integration and delivery processes. This enables faster feedback and early defect detection.

Parallel execution support is another shared capability. Test suites can be executed simultaneously, reducing overall execution time and improving productivity.

Customization is possible in both tools, though achieved differently. Selenium offers customization through code and framework extensions, while TestRigor allows workflow-level adjustments through its platform features.

Finally, both tools can be used in cloud-based testing environments. They support execution on cloud platforms, enabling scalable testing without relying solely on local infrastructure.
These similarities show that despite their different approaches, both tools aim to improve automation efficiency and support modern testing practices.

TestRigor Vs Selenium – Which is Better?

Deciding TestRigor Vs Selenium isn’t about picking a universally “better” tool — it’s about choosing what aligns best with your team’s needs, skillsets, and testing goals.

Selenium has been a cornerstone of test automation for years. In this debate, Selenium stands out for its flexibility, deep customization options, and large ecosystem. Teams with strong programming skills can design robust, complex automation frameworks that handle edge-case scenarios and integrate with almost any tool in the software development lifecycle. For projects requiring highly tailored automation architecture, Selenium remains a reliable choice.

However, TestRigor shifts the conversation in the comparison toward accessibility and efficiency. Because TestRigor uses AI-driven, plain-English test creation, it lowers barriers for testers who aren’t expert coders. This makes test automation adoption faster and reduces long-term maintenance effort. For teams that want to accelerate automation without building frameworks from scratch, TestRigor’s intuitive approach often leads to quicker wins.

In terms of scalability, both tools can scale with your project — but in different ways. Selenium scales through extensibility and coding innovation, while TestRigor scales through platform capabilities, built-in workflows, and reduced maintenance overhead.
Ultimately, TestRigor Vs Selenium comes down to priorities:
If you need deep technical control, maximum extensibility, and a mature open-source ecosystem, Selenium may be the better fit.

If you want faster automation adoption, reduced maintenance, and less reliance on coding expertise, TestRigor could be the better choice.
Evaluating your project requirements, team strengths, and long-term automation strategy will help you make the right decision between TestRigor and Selenium.

An Alternative Tool to TestRigor and Selenium

For teams evaluating TestRigor Vs Selenium but looking for a more unified platform, TestGrid serves as a strong modern alternative.
Why choose TestGrid?

All-in-one testing platform – Supports UI testing, API testing, end-to-end testing, visual regression testing, data-driven testing, cross-browser, responsive, and cross-platform testing.
Scriptless automation – Minimal coding knowledge required, making automation accessible to manual testers and reducing dependency on highly technical resources.
Cost-efficient adoption – Reduces expenses in test design, maintenance, and execution by simplifying automation workflows.
Built-in integrations – Easily connects with CI/CD pipelines, DevOps tools, and defect tracking systems without complex framework configuration.
Cloud testing support – Offers native cloud execution, allowing teams to scale testing without heavy third-party setup.
Salesforce automation – Simplifies CRM workflow automation and reduces the time needed to configure Salesforce test scenarios.
Suitable for all organization sizes – Flexible enough for startups, mid-sized companies, and large enterprises.

In short, teams wanting broader coverage, easier adoption, and reduced maintenance overhead often consider TestGrid as a practical alternative.

Conclusion

The automation landscape continues to evolve, and the TestRigor Vs Selenium discussion reflects a larger shift in how teams approach software testing. Organizations today are not only looking for powerful tools but also solutions that reduce maintenance, speed up test creation, and scale with modern development practices.

Selenium remains a strong and reliable choice for teams that require full customization, deep technical control, and the flexibility of an open-source ecosystem. It works well for complex applications where skilled automation engineers can design and maintain detailed frameworks.
On the other hand, TestRigor represents the new wave of AI-powered automation. It focuses on simplifying test creation, lowering technical barriers, and minimizing maintenance overhead. For teams aiming to scale automation quickly with limited coding expertise, it offers a more accessible path.

Ultimately, the TestRigor Vs Selenium decision should be based on your project complexity, team skillset, long-term maintenance capacity, and testing goals. Evaluating these factors carefully ensures that the chosen tool supports both current needs and future growth.
This blog is originally published at devto

Business Acceptance Testing Guide: Process Flow, Benefits, and Scenarios

Irina Kozlova — Thu, 12 Feb 2026 13:50:39 +0000

A fully functional application can still fail to deliver business value. How?

Even when all your app’s features pass functional and system tests, it may not deliver what business teams or product owners expect, which is driving revenue, meeting compliance obligations, and creating consistent experiences for users.

A checkout flow might work as expected, but the discount applied may be incorrect. This directly impacts business. Over-applied discounts reduce your revenue and margins, and missed discounts can lead to abandoned purchases.

Business acceptance testing helps you solve this issue. It ensures that business processes and rules are enforced correctly before you launch an app.

What is business acceptance testing? How does it work? Who performs it? And what challenges do teams face? We’ll discuss all these and more in this blog.

What Is Business Acceptance Testing and What Is Its Purpose?

Business acceptance testing is a form of acceptance testing where you assess if an app meets the defined business requirements, enables intended business processes, and is ready to be used in the real world.

This testing usually takes place after the core functional checks, like unit, integration, and system tests, and before the production release.

The areas business acceptance testing covers include:

Functional business rules: Pricing logic, discounts, approvals, and eligibility rules
Data correctness and reporting: Transactional data, reports, and dashboards
User roles and authorization: Role-based access, approval authority, and segregation of duties
Compliance and regulatory requirements: Alignment with legal, audit, and policy standards

Business Acceptance Testing (BAT) vs User Acceptance Testing (UAT): Key Differences

Both business acceptance testing and user acceptance testing (UAT) are the final validation stages in the software development lifecycle. But they focus on different success criteria. BAT checks if your app meets your business’s goals, rules, and ROI, and UAT assesses whether your end users are able to do tasks smoothly in your app.

Let’s go deep to understand the difference between the two types of tests.

Real-World Example: Business Acceptance Testing vs User Acceptance Testing
Feature – Invoice generation

Let’s say your app has a feature that automatically generates an invoice after a customer makes a purchase.

Business Acceptance Testing Example: Invoice Generation Scenario

Question it answers: Does this invoice meet business rules and financial expectations?

BAT helps you assess if the invoice generated supports business operations, compliance, and revenue accuracy. Business stakeholders or product owners verify:

If taxes, discounts, and fees are calculated as per business policy
Does the invoice comply with accounting, audit, and regulatory requirements?
Are invoice numbers, dates, and currency formats correct for reporting? User Acceptance Testing Example: Invoice Generation Scenario Question it answers: Can users use this feature correctly and easily?

In UAT, you focus on usability, workflows, and task completion from your end user’s perspective. Users evaluate:

Is the invoice easy to generate and download?
Are invoice details clearly labeled and structured?
Can the invoice be shared, printed, or exported without errors?
BAT testing helps you confirm that the invoice is financially correct and compliant, and UAT ensures users can generate and use the invoice in real-world scenarios.

Key Roles and Stakeholders in Business Acceptance Testing (BAT)

Business acceptance testing needs collaboration of multiple business and delivery roles, where each participant brings a unique perspective to ensure business goals are met.

Business sponsors: These are the executive leaders who fund projects and expect that their investments deliver promised returns. They provide strategic oversight and the final approval before release
Business analysts: They translate your business requirements into testable scenarios and confirm that business rules are validated
Business process owners: Managers who are responsible for processes like sales, procurements, and claims processing, and ensure apps maintain business process continuity during app transitions
End users: Real users who interact with your app and check if interfaces make sense, workflows match actual work patterns, and performance is satisfactory
QA teams: Includes testers who manage test environments, track defects, document issues that impact business outcomes, and ensure issues are resolved before release

Why Is Business Acceptance Testing Important? Benefits of BAT

1. Ensures alignment with business requirements: While testing technical specifications is critical, BAT allows you to ensure your app aligns with the business requirements as well. It assesses if your app’s features are capable of supporting real business processes, policies, and objectives. You can also catch gaps where a functionality fails to meet compliance needs and operational expectations.

2. Confirms real-world usability: Before you release your app, it’s important to make sure it can handle actual business environments. With the help of BAT, you can test how the app’s user flows and functions work across process dependencies and operational constraints such as role-based permissions.

3. Evaluates core business workflows: Business acceptance testing doesn’t just examine functions in isolation. It allows you to analyze if end-to-end workflows behave as expected across systems, teams, and data handoffs. You test the critical processes such as order placement, payment, pricing, and discounts.

4. Improved user satisfaction: Business acceptance testing directly contributes to better user satisfaction. When you rigorously test the critical business flows and outcomes, it reduces the issues users face when carrying out day-to-day tasks. When apps behave predictably and produce accurate results, your users experience fewer errors.

5. Reduced deployment risks: With BAT testing, you can identify critical gaps before your app goes live. It helps you detect issues such as incorrect business rules, compliance mismatches, broken business flows, inconsistent transactional data, and errors in reporting. In turn, you save risks of costly rollbacks and post-deployment fixes.

Business Acceptance Testing Process: Step-by-Step BAT Execution

1. Define test requirements: Engage your business stakeholders, including product owners, sponsors, and business analysts, to understand what business prioritizes the most, whether it’s critical user journeys, rules, KPIs, or compliance needs. Document the required data sets, environment needs, and integrations required to perform the test.

This step is important so you can prevent testing features that don’t actually impact business value.

2. Write clear acceptance criteria and success metrics: Clearly determine the conditions a feature must meet to consider it acceptable. For this, design measurable criteria with policy enforcement and SLA thresholds. Pair them with success metrics such as turnaround times, error rates, or defect leakage so you can ensure everyone has the same understanding of “success” and make effective pass/fail and release decisions.

3. Identify scenarios to prioritize: Every test scenario doesn’t need the same level of attention in business acceptance testing. Therefore, prioritization is necessary so you can optimize resource utilization and test the high-risk processes that directly affect your revenue, compliance, user experience, and core operations.

Also, focus on the edge cases, features with recent updates, and flows that are more prone to failures.

4. Prepare test plan and configure environment: In this step, you design the test plan that outlines scope, roles, timelines, and entry/exit criteria. At the same time, set up your test environment and make sure it closely resembles production, including the integrations and test data.

5. Execute test cases: Allow the business users and domain experts to run the test scenarios in the prepared environment. The main focus is on monitoring if the outcomes match business expectations, not just on technical defects.

In this stage, you record the results, capture screenshots as evidence, and note if there are any deviations.

6. Log defects and resolve them: Identify the issues during execution and log them clearly with business context. Note what failed, where it failed, and how it affects your business outcomes. Then you must prioritize the defects based on risk and severity. This will help you address the critical defects first, so it doesn’t disrupt core user paths and cause regulatory violations.

7. Obtain approval and sign-off: The final step in business acceptance testing is to take formal approval from the business stakeholders that the app meets the requirements and is ready for launch. You document the findings and review test results against defined success criteria before obtaining the final sign-off.

Common Challenges in Business Acceptance Testing (BAT)

1. Lack of Business Tester Availability

Business stakeholders such as product owners or end users have limited availability because of their operational responsibilities. This can delay your testing process or lead to rushed execution and incomplete coverage of critical business scenarios.

Best practice
Try to involve your business testers early and secure time commitments in advance. Keep your BAT testing cycles short and focused. And assign back representatives who can assist in testing if primary stakeholders are not available.

2. Unrealistic or Inadequate Test Data

Note that if your test data is outdated, incomplete, or doesn’t include exceptions or edge cases that reflect real business scenarios. Data that is unreliable can result in inaccurate validation of business rules and surface issues in operational workflows only after your app goes live.

Best practice
Frequently update with test data from production that represents real volumes, input variations, and edge cases. But also make sure your masking mechanisms for sensitive information are in place.

3. Late Discovery of Critical Business Defects

When you treat business acceptance testing as the last checkpoint in the development cycle or rush it just before the release, you may risk discovering critical issues related to business flows and compliance late.

At this stage, fixing issues can be expensive, and with tight timelines, you may have to choose between delaying the release or accepting business risk.

Best practice
You can start business acceptance testing early for critical scenarios and high-impact changes first. Run incremental BAT cycles alongside development so you can identify and address issues simultaneously.

4. Misalignment Between Business Requirements and Testing

This happens when what the business expects and what the team tests are not fully aligned. Vague requirements, assumptions made during implementation, or any gaps between business documentation and test scenarios can cause this disconnect.

Best practice
Engage your business analysts and process owners to convert requirements into testable scenarios. Link each test requirement to BAT scenarios, and use a traceability matrix so testers in the team can ensure the same business expectation throughout the testing cycle.

How AI Improves Business Acceptance Testing Efficiency with TestGrid

TestGrid is an AI-powered software testing platform that automates the creation, execution, and maintenance of end-to-end business acceptance tests. It’s real device cloud allows you to execute tests on a range of Android and iOS devices, as well as multiple browsers, so you can ensure business users have the best digital experience.

Integrations with Jenkins, GitHub Actions, Azure DevOps, and GitLab help your team make BAT a part of your CI/CD workflows. TestGrid’s codeless automation allows QA analysts, product managers, and business leaders to easily describe business scenarios in natural language and automatically convert them into executable tests.

Some of the core features of TestGrid are:

Keep business acceptance tests stable despite UI and workflow changes with self-healing automation
Execute tests in parallel at scale and speed up BAT cycles
Analyze your test results through clear, user-friendly dashboards that highlight issues and performance trends
Test browsers manually to explore unexpected user paths, and detect edge cases and exceptions
This blog is originally published at TestGrid