QA Testing (Quality Assurance)
Definition
QA testing (Quality Assurance testing) is the systematic process of evaluating software against defined requirements to identify defects before they reach production users. Organizations with mature QA practices catch bugs 100x cheaper than post-launch fixes -- a defect found in development costs $80 on average to fix; the same defect found by a production user costs $7,600, according to IBM Systems Sciences Institute research.
QA is not a phase at the end of development -- it is a continuous practice that begins before the first line of code is written and continues through production monitoring. The "test at the end" model produces the worst defect escape rates and the highest remediation costs.
QA testing layers
- Unit tests -- test individual functions in isolation; fast, cheap, comprehensive coverage
- Integration tests -- test that system components work correctly together
- End-to-end tests -- simulate real user flows through the full stack
- Performance tests -- validate behavior under expected and peak load
- Security tests -- SAST (static analysis), DAST (dynamic analysis), dependency scanning
AI QA considerations
AI systems require evaluation beyond traditional pass/fail testing: LLM output quality is probabilistic and must be evaluated against a golden test set with human-defined acceptance criteria. Build an AI eval suite that runs in CI alongside traditional tests -- catching output quality regressions before they reach production.
Related terms
CI/CD (Continuous Integration / Continuous Delivery)
CI/CD is the engineering practice of automatically building, testing, and deploying software every time code is committed to a version control system. Teams with mature CI/CD pipelines deploy to production 200x more frequently with 24x faster incident recovery than teams without automation, according to DORA research -- the most measured indicator of engineering organizational health.
DevOps
DevOps is the organizational and technical practice of unifying software development and IT operations teams around shared tooling, automation, and accountability for the full software delivery lifecycle -- from code commit through production monitoring. Organizations that adopt DevOps deploy software 46x more frequently and recover from incidents 96x faster than those that keep dev and ops siloed.
Observability
Observability is the ability to understand the internal state of a software system from its external outputs -- logs, metrics, and traces -- without modifying the code to answer each new question. Teams with high observability resolve production incidents 3x faster and detect degradations before users report them, according to DORA and OpenTelemetry benchmark data.
Custom Software Development
Custom software development is the design and engineering of software built specifically for an organization''s unique requirements -- not configured from an off-the-shelf product. Custom software reduces time-to-market by 3-6 months compared to enterprise SaaS selection and implementation cycles, and delivers exactly the workflow fit that generic platforms cannot match.
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