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Test Automation, SDLC, Agile & Risk-Based Testing

Test Automation Basics

When to Automate

Automation pays off when a test is:

Signal Rationale
Repetitive across releases Manual repetition is expensive
Part of the regression suite Must run reliably after every change
Data-driven (many inputs) Parameterised tests cover more ground than manual
Stable requirements Changing tests every sprint burns automation ROI
Fast feedback required Slow manual cycles block CI/CD pipelines

When NOT to Automate

Scenario Why
One-off exploratory testing Human intuition finds unexpected bugs automation cannot
Rarely executed scenarios Setup cost > savings
UX / visual / accessibility judgment Automated checks miss nuance
Rapidly changing UI Tests break faster than they are written
Short-lived features Feature may be removed before ROI is reached

Automation ROI Formula (simplified)

ROI > 0  when:
  (Manual cost per run × Runs) > (Automation build cost + Maintenance cost)

Testing in SDLC

Waterfall

  • Testing is a discrete phase after development completes.
  • Test plan written early; execution happens late.
  • Late defect discovery is expensive — high risk.
  • Suitable for: stable, well-defined requirements; regulated domains.

Agile

Practice Detail
Continuous testing Tests written and run in every sprint
Sprint-based regression Automated regression runs each sprint
Three Amigos Dev + QA + Product discuss acceptance criteria before development
ATDD / BDD Tests written from business scenarios (Gherkin) before implementation
DoD includes tests Sprint item is not "done" until tests are written and passing

DevOps / CI-CD

Commit → Unit tests (< 5 min)
       → Static analysis / linting
       → Integration tests (< 15 min)
       → Contract tests
       → Build artefact
       → Deploy to staging
       → Smoke tests
       → E2E regression (< 30 min)
       → Deploy to production
       → Synthetic monitoring

Quality gates block promotion if any stage fails.

See CI/CD Approaches for pipeline-level testing strategy.

QA in Agile

Role in Scrum

Event QA involvement
Sprint Planning Review acceptance criteria for testability
Refinement Clarify edge cases; flag untestable stories
Daily Standup Surface blocked tests, environment issues
Sprint Review Demonstrate tested features
Retrospective Improve process (flakiness, slow feedback, coverage gaps)

Definition of Done (QA perspective)

A story is only done when: - Acceptance criteria are covered by tests (automated or manual) - Regression tests updated - No open High/Critical defects - Test cases reviewed and approved

Continuous Feedback

  • QA provides daily signal: test results, defect trends, environment health.
  • Early defect detection in the sprint is cheaper than fixing after sprint ends.
  • Test coverage reports go into the team dashboard — not just to QA.

Risk-Based Testing

When time is limited, focus effort where failure impact × probability is highest.

Step 1 — Identify risks

Risk area Questions to ask
Business-critical flows What breaks if payment / login / checkout fails?
Recent changes What code changed in this release?
Complex logic Where are the most conditional branches?
Historically buggy areas Where do defects cluster (Pareto principle)?
External integrations Which third-party dependencies are unreliable?

Step 2 — Prioritise

Risk score = Business impact (1–5) × Failure probability (1–5)

High (score 15–25) → Must test in every release
Medium (score 8–14) → Test in most releases
Low (score 1–7)    → Test occasionally / rely on automation

Step 3 — Allocate effort

Priority Approach
High Full test coverage, both positive and negative paths
Medium Happy path + key negative cases
Low Smoke only, or skip manual execution if automated