Quality assurance guides — processes, tools, and techniques for building and running an effective QA practice.
No-Code Testing
Software quality used to be the exclusive domain of engineers. If you wanted automated tests, you needed someone who could write Python, JavaScript, or Java — and ideally someone who understood Selenium's quirks, could configure CI pipelines, and was comfortable debugging stack traces at 11pm. That world is changing
Test Planning
Test estimation is notoriously difficult because the effort required to test something depends on factors that are often unknown at estimation time — complexity, environment stability, defect rate, and whether the feature actually works as described. This guide covers four practical estimation techniques, when to apply each, and how to present
Test Planning
You can never test everything. Risk-based testing is the discipline of deciding what to test first by quantifying the risk of each test area — multiplying the likelihood of a defect by the impact if it occurs. High-risk areas get tested first; low-risk areas may not get tested at all. This
QA Strategy
Most QA processes are inherited, not designed. When you have to build from scratch — new team, new product, or a company that has been shipping without any formal QA — the blank slate is both an opportunity and an obstacle. This guide provides a structured approach to standing up a QA
QA Strategy
Startup QA is not about covering everything — it's about protecting what matters most while moving at startup speed. This guide covers how to decide what to test first, when automation pays off, and how to build a quality culture that scales with the team without becoming a bottleneck.
Test Planning
A test plan is the single document that ties together scope, approach, resources, and schedule for a testing effort. Most teams either skip it entirely or write one so bloated it never gets read. This guide shows you how to write a lean, useful test plan based on the IEEE
QA Metrics
Test automation has upfront costs (writing, infrastructure) and ongoing costs (maintenance) set against time savings (faster test execution, fewer manual regression cycles). The ROI equation is straightforward once you have the right inputs. This post gives you the formula, walks through a worked example, and shows how to calculate break-even.
QA Metrics
MTTD (Mean Time to Detect) measures how long bugs hide before being found. MTTR (Mean Time to Repair) measures how long fixes take. Both are lagging indicators of QA maturity — but both can be systematically reduced through specific practices. This post gives you the definitions, benchmarks, and reduction strategies. Key
QA Metrics
QA investment pays back 4–15× depending on when bugs are caught. Bugs found in production cost 30× more to fix than bugs found in requirements. This post gives you the formulas to calculate your actual ROI and the narrative to present it upward. Key Takeaways * The IBM Systems Sciences
QA Metrics
The DORA metrics measure software delivery performance across four dimensions. Elite teams deploy more often and break things less — because they invest in test automation. This post maps each DORA metric to specific testing practices and shows how to improve your scores. Key Takeaways * DORA's four metrics: Deployment
QA Metrics
Most engineering teams track too few QA metrics (just pass/fail) or too many (every metric their CI tool exposes). This post identifies the five metrics that actually predict QA health, explains what each one tells you, and gives you targets and measurement approaches for each. Key Takeaways * Test coverage
Cross-Browser Testing
CSS looks different in different browsers. Safari lags on newer features. Firefox interprets certain specifications differently from Chrome. Internet Explorer is thankfully gone, but Edge, Safari, and Firefox all have their own quirks. This guide covers the most common cross-browser CSS issues, the tools that catch them, and the techniques