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Practical guides, tutorials, and thought leadership on quality engineering and AI transformation.
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#quality-engineering
19 posts
Track Test Coverage and Defect Metrics in Azure DevOps
How to track test coverage percentage, defect density, and bug metrics in Azure DevOps using dashboards, queries, and reports. Covers code coverage integration, requirement coverage, and using metrics to drive quality decisions.
Implementing AI in Software Testing: A Practical Guide
How to apply Generative AI, ML, and autonomous agents to software testing — covering AI-assisted test generation, smart defect triage, visual testing, and real-world implementation strategies.
Quality Engineering Strategy: A Complete Roadmap for Engineering Leaders
How to build a quality engineering strategy from scratch — covering QE maturity models, shift-left testing, team structure, metrics, and a step-by-step roadmap for transforming quality culture.
Agentic AI and Autonomous Testing: The Future of Quality Engineering
A deep dive into agentic AI for software testing — how autonomous AI agents plan, execute, and adapt test workflows, the current tool landscape, and how to evaluate and adopt agentic testing today.
How to Write Effective QA Test Cases: A Practical Guide
Learn how to write clear, comprehensive, and maintainable QA test cases — covering structure, naming conventions, boundary value analysis, equivalence partitioning, and common mistakes to avoid.
Automated Regression Testing Strategy: A Complete Guide
How to build a regression testing strategy that actually works — selecting what to automate, organising your suite for speed and reliability, managing test data, and keeping your suite healthy over time.
Smart Test Data Generation Using AI: A Practical Guide
How to use AI and LLMs to generate comprehensive, realistic test data — covering synthetic data generation, edge case discovery, PII-safe test datasets, and practical code examples with Claude and open-source tools.
The QA Engineer's Guide to Scrum: Roles, Events, and Best Practices
A practical guide to Scrum from a QA perspective — how testing fits into sprints, what QA does in each Scrum event, how to influence quality from inside the team, and common anti-patterns to avoid.
Top AI Testing Trends QA Engineers Must Know in 2025–2026
The most important AI-driven testing trends reshaping quality engineering — from autonomous agents and self-healing tests to AI-generated code validation and shift-right strategies. What's real, what's hype, and how to act on it.
MCP Servers for QA Engineers: Supercharge Your Testing Workflow
How Model Context Protocol (MCP) servers let QA engineers connect AI assistants directly to their testing tools, CI pipelines, and test management systems — with practical examples for Playwright, Jira, GitHub, and custom QA tooling.
Testing AI-Generated Code: Why QA Matters More Than Ever
As AI-generated code goes mainstream, QA engineers face a new challenge: validating code that was never manually written or reviewed. Here's what changes, what the risks are, and how to build a testing strategy for AI-assisted development.
LLM Testing: How to Test AI Applications and Language Model Outputs
A practical guide to testing applications that use Large Language Models — covering evaluation strategies, prompt regression testing, hallucination detection, latency and cost testing, and the tools QA engineers need to build reliable AI product quality.
Shift-Right Testing: How to Embed Quality in Production
Shift-right testing goes beyond shift-left to embed continuous quality validation in production. Learn canary releases, synthetic monitoring, chaos engineering, and observability-driven QA strategies that catch what staging never will.
What Is Vibe Coding? A QA Engineer's Guide to the AI Development Revolution
Vibe coding — building software by describing what you want to an AI and accepting what it generates — is reshaping how software gets built. Here's what QA engineers need to understand about it, the real quality risks it creates, and how to adapt your testing strategy.
Vibe Testing: The QA Answer to Vibe Coding
Vibe testing applies the same AI-first, natural-language approach of vibe coding to quality assurance — writing tests by describing intent, not scripting steps. Here's how QA engineers can adopt vibe testing workflows, which tools enable it, and where human judgment still matters most.
How Claude.ai Supercharges Your QA Workflow: A Practical Guide
A hands-on guide to using Claude.ai for quality engineering — from writing test cases and generating Playwright scripts to analysing test failures, reviewing test coverage, and building AI-assisted QA workflows. Real prompts, real outputs.
QAOps: Embedding Quality Engineering into Your DevOps Pipeline
QAOps is the convergence of QA and DevOps — continuous quality validation built into every stage of the delivery pipeline. Learn what QAOps looks like in practice, the tools that enable it, and how to transition your team to a QAOps model.
Prompt Engineering for QA Engineers: Get Better AI Output for Testing
A practical guide to prompt engineering specifically for quality engineering tasks — how to write prompts that generate high-quality test cases, Playwright scripts, failure analyses, and test strategies from AI tools like Claude.
Test Coverage Metrics That Actually Matter: A QA Engineer's Guide
Go beyond the 80% code coverage myth. Learn the QA metrics that actually predict software quality: requirement coverage, test pass rate, defect density, and escape rate. Includes formulas, benchmarks, and how to calculate them.
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