Regression Testing
Intelligent Regression Testing
Autonomous agents that maintain, execute, and evolve your regression suite — so every release is validated without growing the manual burden.
Regression Suites Decay Without Constant Attention
Regression testing is the foundation of release confidence. But in practice, suites grow until they're slow, accumulate broken tests that nobody fixes, and gradually lose coverage as the application evolves faster than the tests do. The result: teams either spend enormous effort maintaining the suite or lose trust in it entirely.
Suite Bloat
Tests accumulate over years. Nobody removes obsolete ones. The suite takes longer and longer to run, and a growing percentage of tests validate features that no longer exist.
Silent Coverage Erosion
New features ship without corresponding tests. Old tests break and get disabled. Six months later, 40% of your application has no regression coverage — and nobody realises until something breaks.
Maintenance Consumes All QA Time
In mature suites, 60-70% of QA effort goes to maintaining existing tests instead of writing new coverage. The team is running to stand still.
Slow Execution Blocks Releases
A 4-hour regression suite means one run per day. If it fails, investigation and re-run push the release to tomorrow. Teams start skipping regression entirely for 'small' changes.
A Regression Suite That Maintains Itself
QualityWatcher agents keep your regression suite healthy by self-healing broken tests, identifying coverage gaps, removing obsolete tests, and surfacing the tests most likely to catch regressions for any given change. The suite evolves with your application instead of falling behind.
Self-maintaining
Suite Health
Smart selection
Execution Time
Detected automatically
Coverage Drift
What You Get
Self-Healing Regression Tests
Broken selectors are repaired automatically, keeping the suite green without manual maintenance.
When a UI change breaks a locator, the agent evaluates the DOM and selects the best alternative from the fallback chain. The test continues, the heal is logged for review, and the suite stays healthy. Tests that would traditionally be disabled until someone has time to fix them stay active and contributing.
Coverage Gap Detection
The platform identifies areas of your application that lack regression coverage.
By analysing recorded workflows, existing test coverage, and application structure, the platform surfaces areas with no or thin coverage — 'the user profile settings page has 2 tests while the checkout flow has 47'. This helps teams allocate coverage effort where it matters most.
Impact-Based Test Selection
For any given code change, run only the tests most likely to catch a regression — not the entire suite.
The platform analyses which tests exercise which parts of the application. When a developer changes the payment module, only tests that touch payment flows are selected for the PR run. Full regression still runs on merge to main, but PR feedback is fast and targeted.
Obsolete Test Detection
Identify tests that validate removed features, duplicated scenarios, or unreachable code paths.
Tests that consistently pass regardless of changes, validate UI elements that no longer exist, or duplicate coverage from other tests are flagged for review. Removing them reduces execution time without reducing actual coverage.
Regression Risk Scoring
Every area of the application is scored for regression risk based on change frequency, complexity, and historical failure rates.
The checkout flow that changes weekly and failed twice last month has a higher regression risk than the static about page. Risk scores guide both test selection and coverage investment — ensuring the most volatile and critical areas get the most attention.
Suite Evolution Tracking
Track how your regression suite grows, shrinks, and changes over time.
Dashboards show test count trends, coverage trends, execution time trends, and health trends. Spot patterns like 'we add 50 tests per quarter but disable 30' or 'coverage is flat despite shipping 200 new features'. This data informs testing strategy decisions.
See It in Context
Inherited Legacy Suite
Situation
A new QA lead inherits a 3,000-test regression suite. 400 tests are disabled, 200 are flaky, and nobody knows which ones actually provide value. Running the full suite takes 6 hours.
Outcome
Self-healing recovers most disabled tests. Obsolete test detection flags 300+ tests for review. Impact-based selection reduces PR runs to 20 minutes. The team goes from dreading the suite to trusting it in weeks.
Rapid Feature Development
Situation
A startup ships 3 major features per sprint. The regression suite grows by 100 tests per month, but execution time is becoming a bottleneck and coverage gaps are widening in older areas.
Outcome
Coverage gap detection highlights under-tested areas. Impact-based selection keeps PR runs fast despite the growing suite. Obsolete test detection prevents suite bloat. The team scales coverage without scaling execution time.
Release Confidence Erosion
Situation
After three production incidents caused by regressions that the suite should have caught, leadership loses confidence in the testing programme. The team discovers significant coverage gaps in critical workflows.
Outcome
Risk scoring identifies the highest-risk application areas. Coverage gap detection shows exactly where the suite is thin. Targeted test generation fills the gaps. Within a sprint, the suite catches regressions it previously missed.
How It Works
Analyse Your Suite
The platform ingests your existing regression tests, maps coverage, identifies gaps, and scores regression risk across your application.
Heal and Clean
Self-healing recovers broken tests. Obsolete test detection flags tests for removal. Coverage gaps are identified for new test generation.
Execute Intelligently
Impact-based selection runs the right tests for each change. Full regression runs on merge. Parallel execution keeps times short.
Evolve Continuously
As your application changes, the suite adapts — new coverage is generated, obsolete tests are flagged, and risk scores update based on real failure data.