DEV Community: Irina Kozlova

Automate Biometric & OTP Testing with TestGrid

Irina Kozlova — Thu, 23 Jul 2026 17:54:55 +0000

A fast-growing digital bank relied on its mobile application for customer onboarding, account access, payments, transfers, account recovery, and card management.

With customers using a varied mix of Android and iOS devices, the bank needed stronger assurance that its most sensitive authentication journeys would perform consistently across supported devices, operating-system versions, and application states.

Fingerprint authentication, facial recognition, and one-time passwords secured key customer journeys, including account access, recovery, and sensitive transactions. The bank had automated many of the surrounding mobile workflows with Appium.

Biometric and OTP validation, however, still depended heavily on manual steps. Device access was limited, authentication behavior varied across operating systems, and OTP-based tests often stopped while testers retrieved and entered verification codes.

By introducing TestGrid’s Real Device Cloud into its mobile testing process, the bank expanded authentication coverage across Android and iOS, reduced regression time, and automated more OTP journeys from beginning to end.

The Challenge

The bank needed to validate authentication workflows across a growing range of Android and iOS devices. But its QA team faced several constraints:

Biometric scenarios were tested on a small internal device collection. This limited coverage across Android manufacturers, iPhone models, and operating-system versions. System prompts, application permissions, session handling, and fallback behavior could differ between device combinations.
OTP testing created another bottleneck. Testers often had to leave the banking application, open the device messaging interface, retrieve a verification code, return to the app, and enter the code manually. These interruptions prevented several authentication tests from running unattended.
The team also needed to test negative and recovery scenarios, including rejected biometric authentication, user cancellation, incorrect OTPs, expired codes, resend limits, interrupted sessions, and fallback to PIN or password.
Shared physical devices created further delays. Developers and testers competed for access during release validation, which restricted parallel execution and extended the time required to complete authentication regression.

Why the Existing Approach Fell Short

The bank used emulators and simulators during development. This approach worked for early functional testing. However, it didn’t provide enough coverage for authentication journeys that depended on real device and operating-system behavior.

Biometric and OTP workflows would get affected by system authentication prompts, application and messaging permissions, device keyboards, application switching, session state, and manufacturer-specific interfaces.

The bank needed broader access to real iOS and Android devices, greater parallel testing capacity, and a practical way to extend its existing Appium automation.

The TestGrid Implementation

The bank introduced TestGrid’s Real Device Cloud to expand authentication testing across selected Android and iOS devices.

Its QA team created a priority device matrix based on customer usage, operating-system adoption, authentication risk, and manufacturer coverage, then configured its existing Appium suites to run through TestGrid.

The implementation covered:

Biometric-state validation: On supported Android devices running Android 13 or later, the team used TestGrid’s biometric bypass capability to test successful and failed authentication states. Across the wider Android and iOS matrix, automated application-flow checks were combined with controlled manual validation for:
System prompts
Session expiration
Permission changes
Authentication cancellation
Fallback to PIN or password
Recovery after an interrupted session
Authentication after an application update
OTP automation: Appium tests retrieved OTP messages, extracted the verification code, returned to the banking application, and entered the code automatically. This reduced manual intervention across login, device registration, account recovery, transaction authorization, incorrect-code handling, and repeated OTP requests.
Backend-dependent scenarios: Expired-code and delayed-delivery tests were coordinated with backend services, test data, the messaging provider, and the test environment.
Parallel regression: Authentication tests ran across multiple Android and iOS sessions during the same regression window, reducing delays caused by shared physical devices.
Failure investigation: Logs, screenshots, videos, and device-session evidence helped the team determine whether a failure was linked to the application, device, operating system, message delivery, test data, locator, or session configuration.

The Results

Across the first three mobile release cycles, the bank reported:

3x increase in authentication coverage: The team expanded validation across more device, operating-system, biometric, OTP, fallback, and session combinations.
55% reduction in authentication regression time: Parallel execution and automated OTP entry shortened the time required to complete the agreed regression scope.
70% of priority OTP scenarios automated end to end: Most login, registration, recovery, and transaction-verification flows ran without manual code retrieval or entry.
Earlier detection of authentication issues: The wider device matrix surfaced problems involving biometric fallback, system prompts, OTP entry, application switching, permissions, and session handling sooner.

Fewer issues during UAT: Broader pre-release coverage allowed the team to resolve more login and verification defects before final validation.
Reduced dependence on internal devices: TestGrid supported wider release testing while the bank retained its physical device collection for development and specialized scenarios.

“The biggest change was that authentication testing stopped being the part of the release we had to constantly monitor. OTP journeys could continue without someone waiting to retrieve a code, biometric behavior could be checked across a broader real-device matrix, and failures reached engineering with enough context to investigate immediately. We entered UAT with fewer unknowns and far greater confidence in the journeys customers depended on most.” — Head of Mobile Quality Engineering, Digital Bank

Test Digital Banking Authentication With TestGrid

Your mobile banking application must handle authentication consistently across devices, operating systems, application states, and customer journeys.

TestGrid gives your QA team access to real Android and iOS devices and supports Appium execution for biometric-state validation, OTP verification, transaction authentication, and related mobile workflows.

You can run authentication tests across multiple device combinations, automate OTP retrieval and entry through custom Appium workflows, and review execution evidence when failures occur.

This blog is originally published at TestGrid

Salesforce Testing Guide: Challenges and Solutions

Irina Kozlova — Tue, 21 Jul 2026 17:17:24 +0000

If your organization works with clients and you have a customer relationship management system in place, there’s a chance you’re already using Salesforce.

The global Salesforce services market size stands at $23.0 billion in 2026. And as of 2025, the platform holds approximately 20% of the market share.

But despite being a mainstream platform used by major enterprises, Salesforce poses certain testing challenges. In this blog, we’ll see what those are and the strategies to address them.

Why Salesforce Testing Is Uniquely Difficult

Salesforce testing is harder than testing normal web or mobile apps because not every organization or team uses the Salesforce cloud ecosystem the same way.

Many teams complement the standard features with custom objects, automation, business rules, integrations, and permissions to align with their business operations and goals.

Therefore, testing becomes complex because you have to validate these customizations, plus the regular upgrades and patches that Salesforce releases periodically.

Top Salesforce Testing Challenges

1. Frequent Salesforce releases and seasonal updates

Salesforce delivers three major updates every year, which are Spring, Summer, and Winter. Other than this, it also rolls out periodic maintenance updates and feature enhancements. This can affect your existing workflows, integrations, automations, and user interfaces. And your team may have to repeatedly run tests to ensure business processes work as expected.

Why this happens

Salesforce is a multi-tenant SaaS platform. Meaning, every customer runs on the same version of the platform. Upgrades are automatic and mandatory as per Salesforce’s release cadence. So, you can’t really postpone or skip a release even if it impacts your existing features.

Impact

Updates can increase the maintenance effort for automated test suites as UI elements, metadata, or platform behavior evolve. If you don’t properly test the Salesforce releases, it may lead to production defects.

Example

A Salesforce Summer release may change a standard Salesforce Lightning component used in a custom Lead-to-Opportunity workflow. This can cause your regression tests to fail even when there are no code changes.

Solution

Review Salesforce release notes and test changes in preview sandboxes before every seasonal release
Prioritize automated regression testing for high-risk business flows
Update test scripts regularly to match Salesforce changes

2. Deep customizations and dynamic interface elements

This is one of the major Salesforce testing challenges. Teams who use Salesforce generally depend on extensive custom configuration, custom objects, Lightning components, workflows, APIs, and validation rules.

These customizations can make your testing a lot more complex because Salesforce workflows and environments may behave differently for different departments.

Why this happens

Salesforce is inherently a metadata-driven app where your team can customize objects, fields, business rules, page layouts, and automation according to their business requirements without modifying the core app.

Dynamic rendering can change which UI elements are displayed and how they’re presented based on metadata and user context, making UI testing more complex.

Impact

Too many and frequent customizations may lead to fragile automation scripts, cause unexpected app behavior, and make it difficult for you to ensure consistent functionality across different user roles, business processes, and releases.

Example

You add a new validation rule and modify opportunity page layouts. As a result of this, your existing automation scripts fail because mandatory fields or UI elements changed unexpectedly.

Solution

Document all customizations and update test cases whenever business rules change
Build resilient automation using stable locators rather than relying on dynamic UI attributes to address these Salesforce test automation challenges
Review the changes you made and validate affected business processes before deployment

3. Test environment and data integrity challenges

Maintaining reliable Salesforce test environments and realistic test data is hard. Incomplete or outdated data and inconsistent environments can produce unreliable test results and make it tough for you to assess the business processes properly.

Why this happens

This is one of the Salesforce sandbox testing challenges that happens because Salesforce has multiple sandbox types with different data and storage capabilities.

Developer sandboxes may contain metadata but little or no production data. Partial copy sandboxes might include only selected records. When your production data changes continuously, sandboxes can get outdated unless you refresh and reseed them regularly.

Impact

Poorly maintained environments and stale test data can lead to inaccurate test outcomes, hide production defects, delay your debugging process, and may force your team to spend more time examining issues that can’t be reproduced consistently.

Example

Your customer service team tests an email escalation workflow in an outdated sandbox. Missing entitlement records and obsolete customer data allowed your tests to pass, but the workflow failed after deployment to the production org.

Solution

Refresh your sandboxes regularly to keep metadata and business data aligned with production
Use production-like, masked, and relational test data instead of manually created sample records
Select the appropriate sandbox type as per your testing scope and data requirements

4. Security, governance, and compliance challenges

Salesforce apps usually store sensitive customer, financial, and business data that’s needed to effectively carry out business operations. However, testing security controls, access permissions, and regulatory requirements without exposing confidential information or violating compliance regulations may be difficult.

Why this happens

Salesforce has a layered security model with profiles, permission sets, roles, sharing rules, field-level security, and connected apps. When these configurations update, verifying that every user has the correct level of access gets complex. Moreover, external or third-party integrations may create security gaps.

Impact

Security gaps can lead to unauthorized access or improper handling of sensitive data. This can result in data exposure, compliance violations, regulatory penalties, audit failures, and loss of customer trust.

Example

A financial services company deploys a new customer portal. Testing overlooks field-level security, which allows support agents to view confidential financial records that were intended only for loan officers.

Solution

Check the role-based access, profiles, permission sets, and sharing rules for every user type
Include security and privacy test cases alongside functional and regression testing
Apply masked or anonymized production data to protect confidential information in your test environments

How to Build a Salesforce Testing Strategy to Overcome These Challenges

Following a proper strategy will help you address the Salesforce testing challenges we discussed so far.

1. Determine which critical business workflows to test first

You need to first identify which workflows (across Salesforce Sales Cloud, Service Cloud, Marketing Cloud) have the biggest impact on your operations and business. Try to focus testing on the areas that affect your customers, revenue, and compliance.

Some critical scenarios you must prioritize are:

Lead creation, qualification, and conversion
User authentication, login, and role-based access
Account and Contact management
Order management and fulfillment
Data import, synchronization, and migration processes
Reports and dashboards used for decision-making

2. Build a risk-based testing plan

Now that you know which important flows to test, classify them according to business impact, historical defect patterns, integration complexity, and security risk.

You must emphasize areas with flaky tests caused by poor locator stability or frequent Lightning UI changes (e.g., dynamic IDs, changing DOM structures, or Lightning component updates).

3. Create stable test data and sandbox environments

Next, prepare test environments and data so you can get consistent results across test runs. At this stage, you should implement a test data management strategy that will help you generate, refresh, mask, and maintain realistic and relevant test data.

Apart from this, verify staging environments and sync sandboxes to detect environment drift (like missing metadata, configuration differences, and outdated reference records) before you start test execution.

4. Outline the tests to run

Decide the tests you want to execute to verify functional correctness, integration reliability, and business process continuity for your Salesforce environments.

These are some of the most critical tests you should include to prevent Salesforce testing challenges.

5. Pick out the testing tools

The right test automation tools, unit testing frameworks, API testing frameworks, and test management platforms can help you match the speed of testing with frequent Salesforce releases, evaluate customizations, and spot defects early across Salesforce cloud platforms.

Choosing the right toolset is often the deciding factor in overcoming Salesforce test automation challenges like script maintenance, flaky locators, and slow feedback loops.

Browser automation frameworks like Selenium, Playwright, and Cypress allow you to automate functional and regression testing for Salesforce web apps
Salesforce-native automation systems like Provar let you test Salesforce metadata, Lightning components, and business processes

AI-powered testing tools like CoTester, Testim, and QA Wolf leverage BDD, generative and agentic AI to automatically build and maintain tests and help you reduce script maintenance and flaky tests

DevOps and release management platforms such as Gearset and AutoRABIT support Salesforce CI/CD, automate metadata deployments, and integrate with version control systems

6. Integrate test execution into your Agile and CI/CD workflows

Next, you need to build maintainable test suites using Java, JavaScript, TypeScript, Python, or any language you prefer based on the automation framework you chose. And follow design patterns such as the Page Object Model to improve test maintainability and reusability.

Connect with CI/CD tools like Jenkins, CircleCI, GitHub Actions, or Azure DevOps to trigger automated test runs after every build, code commit, or metadata deployment.

CI/CD integration, as well as following the test automation best practices, will help you get faster feedback throughout the software testing lifecycle and fix issues before release.

Overcome Salesforce Testing Challenges With CoTester

CoTester is an enterprise-grade AI testing agent that works alongside your QA and release teams to test frequent Salesforce releases and dynamic UIs continuously, and helps you prevent common Salesforce test automation challenges

You can record Salesforce workflows directly from live browsers, including login through custom domains, form submissions, button clicks, and page navigation. Then you can automatically convert every interaction into test steps and execute the tests across real devices and browser-based test environments.

Apart from this, CoTester lets you upload Salesforce user stories in PDF, Word, or CSV format, or link them from Jira, and then generates structured tests based on them, which you can review and edit from a natural language interface.

The agent even adapts to Salesforce’s dynamic Lightning UI and updates interactions when components shift, load late, or change structure. Plus, you get step-by-step execution history, screenshots, and error context for efficient debugging

This blog is originally published at TestGrid

Digital Experience Monitoring Explained: Benefits & Tools

Irina Kozlova — Thu, 09 Jul 2026 16:18:28 +0000

Imagine you’re hosting an important business event. You’ve planned every detail to ensure you put on a good show—from the welcome speech to the final presentation. This level of attention to detail is also crucial in the digital world.

Think of it this way:

When your customers interact with your website or mobile app, they expect their experience to be smooth and hassle-free. If it isn’t, they might bounce off the platform and never come back, which reflects poorly on your brand and impacts the bottom line.

So, how do you stay on top of it all?

With Digital Experience Monitoring (DEM).

In this blog post, we’ll explore DEM, its importance, how to create a DEM strategy, and the top tools for monitoring user experience.

Let’s get started.

What Is Digital Experience Monitoring (DEM)?

Digital Experience Monitoring (DEM) is a performance analysis discipline that helps businesses track how well their digital platforms are working from the user’s perspective. It focuses on finding and fixing prevalent issues in real-time user interactions and system performance.

It typically monitors the following parameters:

Experience metrics (page load times, server response time, heat maps, and Time to First Byte)
Resource utilization (CPU usage, memory usage, and disk I/O)
Network performance (latency, bandwidth, packet loss)
Incident detection and crash reports
Customer feedback integration
Real User Monitoring (RUM)
Synthetic monitoring
Application outages

Digital Experience Monitoring vs End User Experience Monitoring (EUEM): Key Differences

DEM and EUEM are closely related concepts, both focusing on the quality of user interactions with digital applications and services. However, they differ in scope and specific priority areas.

EUEM specifically analyzes the performance of a user’s end-to-end workflow across multiple devices, clouds, networks, and applications to improve business productivity. It tracks benchmarks like Time to First Byte (TTFB), session durations, and heatmaps.

Traditional network monitoring shares similarities with EUEM but only measures network-centric metrics such as Mean Time to Repair (MTTR), throughput, and bandwidth utilization.

On the other hand, DEM aims to provide a holistic view of the digital experience, integrating network performance and user-centric data points to ensure optimal service delivery.

Why Digital Experience Monitoring (DEM) Is Critical for Modern Digital Businesses

Digital Experience Monitoring offers many benefits to businesses, including:

1. Bird’s Eye View of Platform Performance

When vital data is centralized, you can answer questions like what applications are in use and how they are performing, how the endpoint is performing, and how the network traffic is behaving.

This shines a light on frequent hesitations, repeated actions, and rapid task abandonment, which can indicate frustration or dissatisfaction among your customers. Understanding these patterns helps you refine your digital offerings and enhance the overall experience.

2. Maximized ROI and Cost Savings

When you have real-time access to the problems existing on your platforms—be it slow page loads, disrupted transaction responses, or application crashes—you can resolve them quickly.

By avoiding prolonged troubleshooting and extended system outages, you can save on operational expenses and minimize potential revenue losses associated with poor user experiences.

3. Proactive Detection of User Experience and Performance Issues

You can use DEM to initiate traffic flows and measure and analyze controlled flows of data to private or public resources. This helps pinpoint the root cause of the problem before it starts impacting the end users. You can implement targeted solutions to mitigate risks, ensuring a seamless and uninterrupted user experience.

4. Enhanced Employee Digital Experience and Productivity

The fewer technical problems employees face, the happier they are. It’s that simple! When IT runs things smoothly and can solve issues immediately—or better yet, predict and address them before they even become problems—it goes a long way toward improving the digital experience.

5. Improved Digital Accessibility and Inclusive User Experiences

By tracking and analyzing metrics related to how customers interact with your digital services using assistive technologies—such as task completion rates, screen reader navigation time, and alt text utilization—you can ensure your platforms are usable by everyone, regardless of their physical or cognitive abilities.

How to Build an Effective Digital Experience Monitoring (DEM) Strategy

This step-by-step guide to creating a Digital Experience Monitoring Strategy teaches you how to harness the power of data to make informed business decisions

1. Define Business and User Experience Objectives

What is it that you’re trying to achieve with DEM?

For instance, if your goal is to reduce your website’s bounce rate by 20% over the next six months, prioritize tasks and allocate resources to monitor and improve factors that directly affect the bounce rate, such as user navigation patterns, content engagement, and page load times.

Create a list of activities to be done to achieve this goal.

For example:

Compress images to reduce load times.
Upgrade keywords to match user intent.
Use strong CTAs to guide user behavior on the website.
Incorporate videos, images, and infographics to keep users engaged.

Define clear menus and internal links to help users find information.
Setting a goal (and subsequent activities) will give your DEM strategy a clear sense of purpose and direction.

2. Identify and Map Critical User Journeys

Once your goal has been decided, map out key user journeys on your website or application.

This includes identifying all the touchpoints where users interact with your platform, such as landing pages, product pages, and checkout processes, and gathering user feedback through surveys, user testing, and direct feedback channels.

For instance, if you have a mobile banking app, your customers should find logging in, checking account balances, and transferring money from the platform smooth and efficient. After all, they represent the core functions that customers rely on.

If you find that the account balance feature takes a long time to load, causing users to log out within ten seconds, prioritize fixing this issue.

3. Select the Right Digital Experience Monitoring Tools and Technologies

A DEM strategy is only as good as the technology you use to implement it. Use a tool that aligns with your objectives and user journeys, equipping you to fetch, visualize, and analyze relevant data about platform interactions and performance issues.

Whether you’re an eCommerce site owner who wants to monitor conversion rates or a SaaS provider who wants to track SLAs, your choice of tool should support bespoke KPIs.

For example, tools like Dynatrace, AppDynamics, and New Relic are chosen based on their ability to provide real-time monitoring, performance analytics, and synthetic testing.

Evaluate different options and select the ones that offer the best mix of features, scalability, and cost-effectiveness. Consider compatibility with your existing tech stack, ease of integration, and vendor support.

4. Implement Real-Time Monitoring, Alerts, and Thresholds

Immediate notifications of and alerts about issues enable you to address problems as they occur before they massively impact users and frustrate them. This proactive approach maintains a high level of service.

For instance, if there’s a sudden spike in server response time, your IT team can receive real-time alerts, prompting an immediate investigation into potential causes like server overload or backend issues.

5. Analyze Experience Data and Take Corrective Actions

Raw data alone doesn’t provide value—it’s also important to extract actionable insights from it. Cultivating the habit of analyzing data regularly helps you stay on top of trends, patterns, and areas needing improvement.

For example, if data analysis shows a high abandonment rate during checkout at a specific step, you can quickly investigate further to spot the underlying issue, such as a complicated account sign-up form, slow page load, or a lack of payment options, and make changes accordingly.

6. Continuously Optimize Digital Experiences Based on Insights

Don’t just implement a plan of action and sit on it—keep updating it as user expectations and technology evolve continuously.

For example, as more users access your site via mobile devices, ensure your Digital Experience Monitoring(DEM) strategy includes mobile-specific performance metrics and user experience considerations.

This exercise will help you incorporate new features, address emerging issues, and adapt to changes in user behavior, which, ultimately, is beneficial for your business as a whole.

7. Collaborate Across Development, QA, IT, and Business Teams

DEM initiatives should involve cross-functional teams, including business stakeholders, IT operations, developers, and even marketers. This collaboration helps ensure that everyone is aligned on goals and objectives and promotes a culture of shared responsibility.

Types of Digital Experience Monitoring (DEM) Tools and Capabilities

Different Digital Experience Monitoring(DEM) tools serve different purposes. Here’s what they do:

Synthetic monitoring tools simulate user interactions to proactively test website, mobile application, and API performance so that they can identify bottlenecks that could potentially impact real users in the future.
Real User Monitoring (RUM) tools collect data on user interactions with digital services and systems using JavaScript-based monitoring or browser plugins. They capture performance metrics and inform optimization efforts based on real-world data.

Endpoint monitoring tools focus on the performance and availability of user devices like laptops, mobile phones, and desktops. They collect and analyze data to determine whether or not they’re correctly configured to deliver superior digital experiences.

Network Performance Monitoring and Diagnostics (NPMD) tools analyze network performance metrics like latency, traffic, and packet loss to find and resolve network-related issues in the digital infrastructure, such as servers, routers, and switches.

Customer Journey Analytics tools are designed to track and analyze users’ paths as they interact with your business’s digital platforms. They monitor the steps customers take from initial contact to conversion, identifying common pathways and drop-off points causing disruptions in the overall experience.
Content Delivery Network (CDN) monitoring tools oversee CDNs’ performance, reliability, and efficiency. They’re essential for distributing content quickly and reliably to users around the globe.

TestGrid for Digital Experience Monitoring: Enterprise Use Cases and Benefits

TestGrid is an AI Powered End-to-end testing platform designed for web and mobile applications. It offers a comprehensive suite of features, including scriptless automation, real-time analytics, multi-platform support, and integration with DevOps tools.

When it comes to Digital Experience Monitoring (DEM), TestGrid’s real-time analytics and monitoring capabilities provide immediate insights into how users interact with your digital applications and services. It exposes network latency, device issues, and performance problems, along with their underlying causes.

Features like automated testing, performance testing, and API testing allow you to identify and address issues quickly, ensuring a seamless digital experience for every user on any device.

The tool’s ability to integrate with various DevOps tools further enhances its utility in maintaining and improving digital experiences. To learn more about using TestGrid to enhance your DEM, book a free demo with our expert team.

Conclusion

As customers increasingly rely on web and mobile services, dependability and speed become critical factors for user satisfaction and business success. That’s why you must proactively resolve any issues that might lead to an unpleasant digital experience.

However, as IT environments become more fragmented and complex, it can become quite difficult to gain end-to-end visibility into user journeys. A robust DEM strategy can give you a unified picture of what’s causing problems in real-time so you can take prompt action.

Remember: Digital Experience Monitoring isn’t a one-off activity—it demands constant effort. So ensure it’s a regular part of your IT and business strategy. Good luck!

This blog is originally published at TestGrid

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