01↗AI as a QA Copilot: Where It Helps and Where Humans Must Decide
A practical look at using generative AI for test design, defect analysis and documentation—without blindly trusting automatically generated answers.
Read article →Insights from practice · 2026
Professional articles
Twenty current topics from Quality Assurance, testing, data, AI and process improvement—practical, clear and focused on real impact.
20articles on quality,
technology and improvement
technology and improvement
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02↗How to Test AI Features That Do Not Return the Same Answer Every Time
Non-deterministic outputs require a different verification strategy from a traditional function with one exact expected result.
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03↗Playwright for Regression Testing: What to Automate First
Automation delivers the greatest value when it protects critical journeys and removes repeated manual work.
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04↗API Contract Testing: Fewer Surprises Between Systems
A small change in data structure can break an integrated process even when every team considers its own service healthy.
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05↗Data Quality in a Marketing Platform: The Error Does Not Start in the Report
Incorrect results can originate during import, transformation, segmentation or export. Checking only the final report is not enough.
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06↗Test Data Without Unnecessary Privacy Risk
Realistic data matters for testing, but copying production information is not automatically the right solution.
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07↗Shift Left and Shift Right: Quality Before Development and After Release
Quality is not created only during a test phase. It starts when requirements are shaped and continues through observation in production.
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08↗Observability Through a QA Lens: Detecting Problems Before Users Do
Logs, metrics and traces are not only operational tools. They also show whether a system can explain its own failures clearly.
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09↗Accessibility Is Not an Extra: It Is Part of Product Quality
An accessible product is clearer, easier to operate and more robust for a much wider range of users.
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10↗Flaky Tests: When Automation Stops Building Trust
A test that passes and fails without a product change gradually stops being taken seriously.
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11↗Risk-Based Testing: More Attention Where It Matters Most
When everything cannot be tested, priority should reflect impact, likelihood and the chance of detecting failure in time.
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12↗Root-Cause Analysis: Stop Fixing the Same Symptom
A quick fix restores service. Understanding the cause reduces the chance that the problem returns.
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13↗Quality Metrics That Support Decisions
The number of tests or defects alone does not show whether the product is safer or the team more effective.
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14↗QA During Requirement Refinement: The Defect That Has Not Happened Yet
An ambiguity discovered during refinement is cheaper than a defect discovered immediately before release.
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15↗Exploratory Testing with Charters: Freedom with Direction
Exploratory testing is not random clicking. A well-designed charter gives the investigation a purpose while preserving room for discovery.
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16↗Release Readiness: A Decision Based on Evidence, Not Feeling
Passing tests is not enough before deployment. The team needs a shared view of change, risk and operational preparedness.
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17↗Cross-Browser and Mobile Testing Without Endless Combinations
It is impossible to manually verify every combination of device, browser, resolution and operating system.
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18↗Performance Testing Before Launch: Start with a Question, Not a Tool
Performance is not only the speed of one screen. It includes response time, throughput, stability and behaviour under peaks.
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19↗A Bug Report That Speeds Up the Fix Instead of Starting a Long Conversation
A strong defect report enables another person to understand, reproduce and assess the impact of the problem.
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20↗A Quality Retrospective: Small Changes That Actually Last
Improvement is not a long list of ideas. It is choosing one concrete change, testing it and returning to the result.
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