DEV Community: Alexi

5 Hats of QA

Alexi — Fri, 25 Sep 2026 13:39:23 +0000

When we say "QA", most people think about a tester. But QA can try on many hats, from problem-prevention to curious explorer.

Let’s take a look at the 5 meanings (or “hats”) of QA 👇

Quality Assurance

Aimed to ensure that quality is maintained at all stages of development and that the final product meets requirements.

➡️Objective: To minimize defects during development while also ensuring they are fixed before product release.

Quality Questioner

A mindset that drives clarity and prevents costly mistakes through curiosity and critical thinking. QA professionals challenge assumptions, ask questions, and ensure that what’s being built truly aligns with user needs and business goals.

➡️ Objective: To identify gaps and risks before they turn into bugs or misunderstandings.

Quality Addicted

People genuinely obsessed with excellence, the kind who notice inconsistencies others overlook. They continuously seek to improve processes and products. “Good enough” is never enough.

➡️ Objective: To cultivate a culture of continuous improvement and excellence.

Quality Guardian

Acting as guardians of stable quality, they collaborate across teams to prevent technical debt and last-minute crises from reaching production.

➡️ Objective: To safeguard product reliability and user trust through proactive teamwork.

Quality Analyzer

Provides valuable information for designing better tests and improving quality.

➡️ Objective: To turn QA data into business value by optimizing processes and decisions.

Usually, QA professionals combine several of these qualities, switching between roles depending on the project’s needs.

If you’re a QA professional, which “hat” fits you best, or do you wear them all? Share your thoughts in the comments 👇

The Pesticide Paradox in Testing: Why It Happens and How to Avoid It

Alexi — Sun, 20 Sep 2026 15:58:49 +0000

Imagine running the same automated test suite over and over. At first, it catches bugs, and everything seems great. But then, over time, it stops detecting anything new. Does this mean your product is flawless? Not really. This is what we call the pesticide paradox in testing. This concept, first introduced by Boris Beizer, explains that if you continue running the same tests over time, they will eventually stop identifying new bugs.

*So, why does this happen? And more importantly, how can you prevent it? *

*Why Does the Pesticide Paradox Happen? *

Applications are constantly evolving with bug fixes and feature updates, and tests need to be updated regularly. Running the same old tests and using the same test data might overlook important issues.

*How to Avoid the Pesticide Paradox *

Regular test maintenance: Treat your test scripts like living documents. Update and add to them regularly to keep up with new features, bug fixes, and app changes.

Review your test suite: Test scripts can contain bugs that divert focus away from developing the application itself. To avoid this, it's essential to review the test suite periodically. Retrospective analysis can help identify recurring issues and patterns,

The same tests need to be run manually from time to time: Despite focusing on automation, human intuition is vital to bypass the pesticide paradox—it helps uncover defects that automation scripts might miss.

Effective bug analysis: Take the time to investigate each bug thoroughly to understand its root cause of issue. Through bug analysis, understand the problem area and curb the potential recurrence of similar issues.

The pesticide paradox cannot be avoided by simply running more tests or writing better scripts. It’s about continuous improvement, adaptability, and recognizing that testing is never truly “done.”

Stop Paying Twice for the Same Feature: The Hidden Cost of Slow Feedback

Alexi — Sun, 20 Sep 2026 15:54:56 +0000

Imagine spending $50,000 on a new feature, only to discover a week after launching that a small logic error from the very first day of development made it unusable for 30% of your users.

Now your developer must spend time reworking the same feature, fixing it, and redeploying it. This delay can slow down other planned work and disrupt the entire release cycle.

This isn’t a coding problem, it's often a feedback problem. When insights take too long to travel from QA to Dev, costs don't just add up, they multiply. Continuous feedback loops act as your financial safety net, catching the "small stuff" before it becomes an expensive disaster.

The Price of Silence: Why a Bug Found in Production Costs 100x More Than One Found in Development

According to the Systems Sciences Institute at IBM, the cost to fix software bugs increases exponentially the later they are discovered in the development lifecycle.

Other industry analyses reinforce this, estimating escalations of 30x or more due to the compounding challenges and risks involved.

By fostering immediate communication, feedback loops minimize these risks, ensuring issues are nipped in the bud and keeping your budget intact.

Build-Measure-Learn: How We Use the Lean Startup Methodology to Protect Your Budget

Drawing from the Lean Startup principles by Eric Ries, the Build-Measure-Learn loop emphasizes rapid iteration based on real data. We apply this by building minimal viable features, measuring their performance through user interactions and metrics, and learning from the results to refine quickly.

This approach safeguards your budget by avoiding over-investment in unproven ideas. Instead of committing full resources upfront, we validate assumptions early, pivot as needed, and allocate funds more efficiently.

The Result: Faster Releases, Predictable Costs, and a Product Your Users Actually Love

When feedback loops are optimized, the outcomes are transformative. Releases accelerate because issues are resolved in hours, not weeks, enabling more frequent updates without compromising stability. Costs become more predictable as rework and emergency fixes decline, enabling better budgeting and resource allocation.

Ultimately, this creates a virtuous cycle: a reliable product drives business growth, reinforcing the value of your initial investments.

Hallucination Management: From "Vibes" to Trust Engineering 🛡️

Alexi — Tue, 15 Sep 2026 08:32:48 +0000

In 2026, the central challenge of moving Generative AI into production isn't what the model can do, but how we control what it invents. Hallucinations are now recognized as an intrinsic property of autoregressive models, and for enterprise applications, they represent a critical business and reputational risk.

To transform unpredictable AI into a reliable toolset, modern QA processes have evolved into a system built on three engineering pillars:

1. Multi-Stage Self-Review Frameworks

Engineering research confirms that separating the cognitive tasks of "generation" and "critique" (Self-Correction) significantly reduces logical and semantic errors. By using specialized validation agents, systems can now detect inconsistencies during the initial response phase. This is especially vital in complex analytical tasks, such as translating natural language into high-precision database queries like SQL.

2. Moving from Subjectivity to Hard Metrics

We are moving past "vibe checks" to standardized, quantifiable metrics:

Faithfulness: Measuring the exact proportion of claims supported by the retrieved context.

Answer Correctness: Direct comparison against a human-verified "Ground Truth" or "Golden Dataset".

Architectural Coherence Score (ACS): A specialized metric evaluating the model's ability to maintain design consistency across massive codebases or complex documentation.

3. Human-in-the-Loop (HITL)

While AI can automate repetitive tasks, it cannot replace critical thinking, creativity, and contextual awareness of human QA engineers. Instead, AI should be seen as a tool to enhance their capabilities, allowing them to focus on more strategic and complex challenges. The role of the human QA engineer is shifting toward who:

Verifies Ground Truth: Curating the high-fidelity datasets that serve as the foundation for all automated evaluations.

Configures LLM-as-a-Judge: Tuning advanced models (like GPT-5 class judges) to evaluate outputs, achieving up to 80-90% agreement with human experts.

Automates Quality Gates: Integrating regression detection directly into CI/CD pipelines to block faulty updates before they reach the user.

AI reliability is not an accident. It’s a deliberate engineering choice. By integrating advanced observability (Maxim AI, Langfuse) with strategic human oversight, we transform unpredictable models into resilient business assets. Measurable trust is the only currency that allows AI to scale.

Manual vs. Automation Testing: Where Should You Invest First?

Alexi — Thu, 03 Sep 2026 15:32:15 +0000

Automation testing rarely stands on its own. Writing reliable automated tests requires a solid understanding of manual testing scenarios and outcomes. Moreover, automation still struggles in areas that require human judgment, empathy, and creativity.

Common Mistakes Killing Your ROI

❌ Trying to automate everything on day one

❌ Ignoring test maintenance costs (they add up quickly)

❌ Automating unstable features that change weekly

❌ Choosing tools based on trends instead of specific project needs and team capabilities

❌ Forgetting that manual testing validates whether software aligns with business intent, not just technical correctness

Start with manual testing if:

✓ You're launching an MVP or prototype

✓ Features are evolving rapidly

✓ Your team lacks automation expertise

✓ The product has a short lifecycle

✓ You need quick validation without infrastructure setup

Invest in automation when:

✓ You have stable, repetitive test cases

✓ Regression testing consumes significant manual effort

✓ You're scaling rapidly (users, features, releases)

✓ CI/CD integration is critical

✓ Long-term product maintenance is planned

The Hybrid Strategy

Manual testing is irreplaceable for user experience and visual inspections, while automated testing shines in handling repetitive tasks and large-scale projects. Used together, they complement each other and ensure stronger product quality.

AI-Assisted Testing

Up to 56% of companies are already investigating AI adoption in testing, with 38% viewing AI as a solution for tester shortages.

But my point is: It’s not a competition between people and AI. When used together, they simply make it easier to keep up the pace and still deliver solid results.

How to Improve the Testing Process Across Multiple Devices

Alexi — Fri, 28 Aug 2026 14:14:26 +0000

Enhancing the testing process across different devices is crucial to ensuring high software quality and compatibility. Below are several strategies and recommendations to improve this process:

1. Establish a Clear Testing Strategy

Create a test plan: Identify key aspects of the application that need testing, such as functionality, performance, security, and usability. Determine the target devices and platforms for testing and establish a testing schedule.

2.Set Up a Comprehensive Test Environment

Emulators & simulators: Use emulators and simulators for mobile testing to quickly verify functionality across different devices.

Physical devices: Whenever possible, test on real devices to obtain more accurate results.

3.Leverage Test Automation

Automated testing: Develop automated tests for core application functionalities to accelerate testing.

Cross-platform testing tools: Utilize tools like BrowserStack or Sauce Labs to test applications across different devices.

4. Implement CI/CD Practices

Continuous Integration & Deployment: Integrate testing into the CI/CD pipeline to automate test execution and accelerate release cycles.

5.Consider Device Diversity

Target audience analysis: Identify the most popular devices and platforms among your users and prioritize testing on them.

Varied configurations: Ensure testing covers different screen sizes, resolutions, OS versions, and hardware specifications.

6.Keep Test Cases Up to Date

Test case updates: Regularly review and update test scenarios to reflect application changes and support new devices.

7.Collect and Analyze Test Data

Logs & reports: Gather testing logs and reports to analyze issues and identify trends.

User feedback: Consider user feedback on application performance across devices to refine the testing approach.

8.Maintain Proper Documentation

Testing process documentation: Document testing procedures to ensure consistency and clarity across the team. Regularly generate test reports to track progress and identify areas for improvement.

9. Invest in Team Training

Training & skill development: Train testers on new tools and methodologies to improve their efficiency in testing across multiple devices.

Collaboration: Encourage close collaboration between developers and testers to better understand and resolve issues.

By following these recommendations, you can significantly improve testing efficiency across multiple devices, leading to higher software quality and a better user experience

Myths About Automated Testing

Alexi — Fri, 28 Aug 2026 14:08:09 +0000

Automated testing has become essential to modern software development, but it’s often misunderstood. Despite its benefits, several myths persist that can mislead organizations when implementing test automation. Let’s take a look at some myths about automated testing.

Myth1

*100% Automation is Possible *

The idea that full automation is achievable is a common misconception. While automation is incredibly effective for repetitive, well-defined tasks, it cannot fully replace manual testing. Certain tests, such as exploratory testing or usability tests, are inherently impossible to automate due to their need for human judgment and creativity.

Myth 2

*Developers Are Better Than Automated Testers *

While developers may have an advantage in programming skills, testers bring a unique mindset focused on uncovering bugs and verifying system functionality. Automated testers are specialized in creating reliable testing frameworks and ensuring code quality, offering a crucial perspective that developers may not prioritize.

Myth3

*Automation Is Expensive *

Although setting up automation can require an upfront investment, when done right, it can significantly reduce overall testing costs. Automated testing reduces the need for repetitive manual testing and speeds up the testing process, ultimately delivering more cost-effective long-term results.

Myth 4

*Automation Is Only for Large Projects *

Some people believe automation only benefits large-scale projects, but this is not the case. Even smaller projects can benefit from automation by improving test efficiency, reducing human error, and ensuring consistent test execution. It can help maintain high-quality standards across projects of all sizes.

How a Balanced Testing Pyramid Turns QA Into a Business Advantage

Alexi — Fri, 26 Jun 2026 09:56:46 +0000

Are slow releases, rising QA costs, and late-stage defects slowing your product down?
If bugs still reach production despite significant testing effort, the issue is rarely “not enough testing”.
More often, it’s a lack of a coherent testing strategy.

The Software Testing Pyramid is more than a model; it provides a clear framework for balancing test types to maximize efficiency, speed, and reliability.

The 4 Core Layers: A Detailed Breakdown

The pyramid is divided into 4 levels (from bottom to top):

unit testing (module);
integration testing;
system testing;
user acceptance testing.

Level 1: Unit Testing

Unit tests verify the smallest pieces of your application. When a unit test fails, it pinpoints the exact location of the error, providing immediate, actionable feedback to developers.

Level 2: Integration Testing

This testing level aims to identify interface faults between modules/functions. Integration tests can interact with the code without using any UI actions, typically performed by developers.

Level 3: System Testing

System testing holds great significance as it ensures that the application meets the technical, functional, and business requirements defined by the customer.

Level 4: User Acceptance Testing

This level of testing verifies that the software product meets end-user expectations and is ready for real-world use in production.

The Anti-Patterns: Common Traps to Avoid

Many teams inadvertently adopt "anti-patterns" that invert the pyramid, leading to disastrous consequences for their testing efforts.

The Testing Ice Cream Cone

This is the most common and dangerous anti-pattern. It features a massive layer of slow manual or automated E2E tests, a thin layer of integration tests, and a tiny, almost non-existent base of unit tests. Teams fall into this trap because testing through the UI feels intuitive, and there's often pressure to "just test the feature" without investing in foundational unit tests.

Consequences: Slow feedback loops, a flaky test suite that no one trusts, high maintenance costs, and bugs being discovered late in the development cycle, when they are most expensive to fix.

The Testing Hourglass

In this anti-pattern, teams have a good number of unit tests and E2E tests, but a "pinched" middle with very few integration tests. This often happens when developers focus on their units, and QA focuses on the full system, leaving the crucial connections between components untested.

Consequences: While individual units work and the overall system might appear to function, bugs remain hidden in the interactions between two elements of the system. This can lead to subtle, hard-to-diagnose issues in production.

Optimizing your testing strategy requires experience, context, and the ability to balance speed with risk.

Testing vs Checking: What’s the Difference?

Alexi — Thu, 25 Jun 2026 10:09:15 +0000

In the world of quality assurance, we often hear these two terms thrown around: testing and checking. Some use them interchangeably. But they are not the same. Testing and checking are two different types of activities. They deserve a distinction.

Let’s break it down.

Testing is a cognitive activity. It’s about learning the product, exploring behaviors, forming hypotheses, asking questions, and making informed inferences. Testing is not just about confirming what you already know; it’s about discovering what you don’t.

📌 Testing:

Has an open-ended purpose: to understand behavior and identify risks

Includes unexpected observations

Relies on human thinking and interpretation of results

May include checks, but is not limited to them

Checking is a process of confirmation, validation, and verification. It is the process of verifying specific facts about the product, algorithmically. Checking is part of testing.

📌 Checking:

Follows a precise algorithm: “If X, then Y”

Can be fully automated

Doesn’t provide a deep understanding, only signals that something matches (or doesn’t match) expectations

It is a part of testing, but not testing in the complete sense

So we can say that

🔸 Testing is a human activity.

🔸 Checking is something a machine can do.

💡 Why is this distinction important?

Understanding the difference between testing and checking is the key to an informed, flexible, and truly effective approach to quality assurance.

If we confuse the two, we might wrongly assume that running automated checks is enough. But bugs often hide outside the expected, and it takes human judgment and exploration to uncover them.

This is precisely why we distinguish between the aspects of the testing process that can be performed by machines and those that require QAs.

AI and ML in Software Testing: How These Technologies Are Transforming QA

Alexi — Mon, 08 Jun 2026 06:09:29 +0000

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the field of software testing and quality assurance (QA). Traditional testing approaches are giving way to more efficient methodologies that leverage AI and ML to improve defect detection.

Integrating AI and ML into QA processes yields several significant benefits:

Faster Test Execution: AI-powered tools can execute tests in parallel, speeding up the testing cycle. This reduces the time to get feedback on code changes.
Reduced Manual Effort: Routine tasks like test runs, data entry, and bug assessment can be handled by AI.
Cost Savings: Automated testing with AI can lead to significant cost savings. Tests can run continuously without human intervention.
Enhanced Defect Detection: Machine learning models can analyze test data to detect patterns that indicate potential defects. This allows QA teams to address issues before they affect users, improving software reliability.

While AI offers many advantages, it also comes with challenges and limitations:

Implementing AI in testing can be complex. Setting up AI models or tools requires data preparation, configuration, and often a certain level of technical expertise.
AI systems, especially deep learning models, can make decisions that are hard to interpret.
If not carefully validated, AI can produce false positives (flagging something as a defect when it’s not) or false negatives (missing a real d
efect).
AI tools often require ongoing maintenance and updates.

To successfully integrate AI and ML into QA, you may consider the following best practices:

Define what you want to achieve with AI in QA. Whether it’s reducing test execution time, improving defect detection - having clear goals will guide your implementation.
Don’t rely solely on AI automation or solely on manual testing. Instead, adopt a hybrid model in which AI handles repetitive or data-intensive tasks, while humans handle the creative, judgment-based aspects.
Identify which testing tasks will benefit most from AI. Start with high-impact areas like automating large test suites, predicting defects, or handling visual testing.

AI and ML are transforming QA in profound ways. Those who embrace these technologies and integrate them thoughtfully into their testing workflows will gain a competitive edge in delivering high-quality software faster.

Welcome to the Fast-Food Era of Testing: Over-Weight by Tests

Alexi — Mon, 25 May 2026 07:24:59 +0000

In software testing, it’s crucial not only to ensure the quality of the product but also the quality of the testing process itself. This is assessed using QA metrics—indicators of test effectiveness, code coverage, and teamwork. One key metric is test coverage—the percentage of code or requirements covered by tests. QA professionals must stay flexible, assess project needs, and clearly communicate priorities to PMs. They should not blindly follow test plans or documentation; instead, they should apply critical thinking and common sense.

I recently attended a testing conference where the concept of the “Fast-Food Era of Testing—being overwhelmed by tests” caught my attention.

I want to share it with you because I completely agree with the speaker’s point: achieving 100% test coverage remains unachievable, so the task of QA is to strike a balance.

*Fast-Food” Testing: 100% Code Coverage ≠ 100% Bug Free Product *

There are some misconceptions about high test coverage, such as the assumption that it guarantees bug-free software. Blindly pursuing 100% test coverage can create more problems than it solves. Here’s how:

False Sense of Security:

Achieving 100% coverage may seem like a victory, but there may be hidden problems. Bugs often hide in areas that automated tests can’t predict, such as untested edge cases or real-world scenarios, because test coverage only shows how much of the app’s functionality is covered by tests, not how good those tests actually are.

Maintenance Cost:

More tests mean higher maintenance costs, as each test needs to be updated and managed regularly.

Diminishing Returns:

As you get closer to 100% coverage, it becomes harder to add meaningful tests. At some point, you’re just writing superficial, redundant tests to tick a box or boost metrics. You might even start writing tests that cover lines of code, but don’t test the actual functionality.

Opportunity Cost:

Time and resources spent on maintaining excessive tests could be better utilized elsewhere, such as improving tests quality or developing new features.

Business Risk:

Over-reliance on a large number of tests can introduce business risks, such as delayed releases or increased costs, impacting overall business performance.

The “Health” plan

To keep testing healthy, think of a balanced “meal plan” for QA:

H

— High-Value Focus

Focus on Minimum Viable Testing (MVT). Add only tests that bring clear business value.

E

— Eliminate Junk

Find and change flaky, remove duplicate, or low-value “fast-food” tests.

A

— Adopt Routines

Hold regular pruning sessions and refactor to keep the test suites clean and reliable.

L —

Level Coverage

Balance coverage at all levels—unit, integration, system. Give priority to high-risk, business-critical flows rather than chasing 100 % coverage.

T

— Track Relevance

Design tests with purpose. Continuously check if each test still aligns with current requirements and business needs.

H

— Healthy Mindset

Apply the 80/20 rule: 20% of well-designed tests often catch 80% of critical issues — so value quality over quantity.

Manual vs Automated Testing: A False Dichotomy

Alexi — Mon, 11 May 2026 07:21:53 +0000

In testing, we often talk about the “balance between manual and automated testing,” but this is a misleading way to think about it. Testing is not about dividing work into manual or automated. It is a process of exploring and verifying the product.

When faced with a certain task, the focus should not be on “balance,” but instead on which tools and approaches are best suited to accomplish that task.

Manual testing is indispensable, as manual test cases serve as the foundation for future automation. Manual testing revolves around the aspects that automated testing can't cover. These include visual application checks and the ability to test specific user experience-based scenarios that are impractical to automate. On the other hand, automated testing has its advantages. It eliminates the human factor, allows you to use the same scenarios repeatedly, and is much faster than manual testing.

Moreover, manual testing experience is invaluable for automation testers. Understanding the app from a user's perspective helps automation testers write better scripts and prioritize what to automate.

However, this is not a question of balance, but of effectiveness in choosing the right approach for the current product needs.

For example, when you go to the store, you don’t think about “balancing walking and biking.” Instead, you consider whether the bike will save time or effort, whether you’re in a hurry, or if it’s more convenient to carry your groceries by bike. You simply choose the option that best accomplishes the task.

The key point is to have a diversity of tools and approaches. This diversity enables better adaptation to the product’s complexity. It is diversity, not balance, that is the key to quality testing.