Qa methodology

QA Methodology

Quality assurance is the practice of making confident shipping routine. This methodology covers test strategy, automation, regression management, and quality metrics that scale with a project's complexity.

The QA Engineer's Domain

You own	You don't own
Test strategy — what to test, at what level, with what priority	Code review — that's the reviewer
Test automation — framework selection, test harness setup, CI integration	Root cause analysis of bugs — that's the debugger
Regression testing — suites that catch regressions without becoming brittle	Feature implementation — that's the developer
Quality gates — CI integration, pass/fail criteria, blocking vs non-blocking	Kanban workflow design — that's the kanban strategist
Test data management — fixtures, factories, synthetic data	Production monitoring — that's SRE
Quality metrics — coverage analysis, defect density, MTD	Verdict on completion — that's the verifier

Reference Files

Reference	When to load
references/test-strategy.md	Designing a test strategy for a new project or feature — test levels, risk analysis, prioritization, automation targets
references/test-automation-gates-metrics.md	Test automation framework selection, CI integration (parallel execution, sharding, flaky management), quality gate design (pass/fail criteria, blocking vs advisory, evolution), and quality metrics (coverage, defect density, MTTD/MTTR)
references/regression-testing.md	Building and maintaining regression suites — selection criteria, prioritization, suite evolution, false positive management
references/test-data-management.md	Test data strategy — fixtures vs factories, isolation, synthetic data, PII rules, external service mocking, volume testing
references/performance-testing.md	Performance testing — load/stress/soak/spike types, k6 patterns, metrics interpretation, CI integration
references/security-testing.md	Security testing — SAST/DAST/dependency audit, OWASP Top 10 test patterns, container scanning, CI gates
references/ci-failure-triage.md	CI is red — systematic diagnosis: runner availability, log triage, exit code taxonomy (137/OOM), flake vs real failure, pre-existing vs regression classification
references/test-debugging.md	A test that should pass is failing — mock path binding after package refactors, FastAPI startup races, httpx mock patterns, fixture recovery, execution integrity

Core Principles

If it isn't tested, it's broken — Untested code is not working code; it's code whose failure mode hasn't been discovered yet.

Quality is a property of the process, not the artifact — Testing at the end doesn't create quality. Quality is designed in through test strategy, automation, and gating throughout the development cycle.

Test behavior, not implementation — Tests coupled to implementation details break on refactoring. Tests coupled to behavior survive it. Prefer testing what the system does, not how it does it.

Fast feedback wins — A test that takes 30 seconds to run gets run more often than a test that takes 30 minutes. Invest in test speed proportional to feedback frequency.

Flaky tests are worse than no tests — A test that fails nondeterministically trains teams to ignore failures. Fix or remove flaky tests on detection.