Catalog
concept#Quality Assurance#Software Engineering#Governance#Observability#Reliability

Quality Assurance (QA)

Quality assurance comprises strategies, processes and activities that ensure products and services meet requirements and that defects are detected early.

Quality Assurance (QA) is a comprehensive approach for systematically ensuring product and process quality.
Established
Medium

Classification

  • Medium
  • Organizational
  • Organizational
  • Intermediate

Technical context

CI/CD pipelines (e.g., Jenkins, GitHub Actions)Test automation frameworks (e.g., Selenium, Playwright)Observability tools (e.g., Prometheus, Grafana)

Principles & goals

Prevention over correction: avoid defects through design and process.Measurability: make quality transparent with metrics.Continuous improvement: implement learnings from incidents and tests.
Iterate
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Focus on metrics instead of actual customer experience.
  • Silos between development and QA lead to ineffective collaboration.
  • Late QA involvement increases defect remediation costs.
  • Shift-left: involve QA early in requirements and design.
  • Automate where it delivers stable value.
  • Use metrics to prioritize actions based on data.

I/O & resources

  • Requirement specifications and acceptance criteria
  • Test environments and test data
  • Access to monitoring and log data
  • Test reports and metrics
  • Release and support decisions
  • Improvement actions and tickets

Description

Quality Assurance (QA) is a comprehensive approach for systematically ensuring product and process quality. It combines organizational responsibility, defined testing and improvement processes, and metrics for transparency. QA aims to reduce defect sources and embed continuous improvement across development and operations.

✔

  • Reduction of production defects and related costs.
  • Improved customer satisfaction through more reliable products.
  • Higher release predictability and reduced downtime.

✖

  • QA may require organizational change costs and training effort.
  • Excessive formalization can hinder flexibility and innovation.
  • Not all defects can be fully covered by tests.

  • Defect density

    Number of found defects per code or function unit as a quality indicator.

  • Mean Time to Detect (MTTD)

    Average time until detection of a defect in test or production.

  • Test coverage

    Percentage of code or application areas covered by automated tests.

Enterprise QA program

Organization implements central QA governance with metrics, training and tooling to unify practices.

CI-driven test automation

Teams integrate comprehensive test suites into CI pipelines to prevent regressions before merges.

QA as a service for product teams

Central QA team offers consulting, reviews and test automation as a service to decentralized product teams.

1

Assess current QA practices and tooling

2

Define shared quality metrics and SLAs

3

Build automated tests and integrate into CI

4

Introduce governance, reviews and continuous monitoring

⚠️ Technical debt & bottlenecks

  • Outdated test infrastructure causing slow feedback loops.
  • Poorly maintained test data stores with inconsistent states.
  • High number of flaky tests without investment in stabilization.
Lack of test dataInsufficient automation capacitySiloed coordination between teams
  • Searching all defects by manual testing instead of risk-based prioritization.
  • Manipulating metrics to hit targets instead of improving quality.
  • Applying automation to volatile UI elements without stabilization.
  • Too high expectations of automation without maintenance capacity.
  • Ignoring organizational causes in favor of technical measures.
  • Viewing metrics in isolation without context and root-cause analysis.
Test design and test automationFamiliarity with CI/CD and DevOps practicesAnalytical skills for root-cause analysis
Testability of componentsFast feedback cycles in CI/CDMeasurability and transparency of quality
  • • Budget and time constraints for test automation
  • • Regulatory requirements for audit and compliance
  • • Legacy systems with poor testability