Memory of how your app fails.
Every failure categorized, every root cause learned, every flake pattern remembered. Durable application context for the team's humans and AI agents — not advice from a vanilla LLM.
Surface the top failure patterns and their root causes
247 failures triaged · grouped into 6 patterns
95%
Categorized
90%
Root-caused
12
Flagged flaky
3
Regression
Same patterns fed to your AI agent via MCP — tr_failure_patterns surfaces them on every coding turn.
How AI Analysis Works
Transparent, explainable AI built for QA teams
Data Collection
We collect test logs, stack traces, screenshots, and execution metadata from all your test runs
Pattern Recognition
Our ML models analyze failures across time, identifying recurring patterns and correlations
Root Cause Analysis
AI determines the most likely root cause and provides actionable recommendations
Types of AI Insights
Comprehensive analysis across all dimensions of test quality
Failure Categorization
Automatically categorize failures into: Infrastructure, Application, Test Code, Environment, or Flaky
Flaky Test Detection
Identify tests with inconsistent results. Get confidence scores and historical pass/fail patterns
Regression Alerts
Detect when new code breaks previously passing tests. Pinpoint the exact commit or deployment
Performance Degradation
Identify tests that are slowing down over time. Get alerts before they impact CI/CD pipelines
Pattern Anomalies
Discover unusual patterns in test execution. Detect cascading failures and correlated issues
Impact Analysis
Understand the blast radius of failures. See which features, users, or environments are affected
Proven Accuracy
Backed by data from millions of test executions
Correctly identifies the type of failure
Pinpoints the actual cause of failure
Average reduction in debugging time
How We Measure Accuracy
We continuously validate our AI models against human-labeled data and user feedback, and recalculate our accuracy metrics with every model release.
Sample Insights
See what AI insights look like in practice
login_with_oauth test is flaky
This test has failed 3 times in the last 10 runs, but only in the CI environment. Likely cause: timing issue with OAuth redirect.
Add explicit wait for OAuth callback or increase timeout threshold
Payment flow broken after deployment #1234
5 payment-related tests started failing immediately after deployment #1234. All failures show 'stripe.confirmPayment is not a function' error.
Stripe SDK version mismatch detected. Rollback to previous version or update test mocks.
Database query tests 40% slower this week
Average execution time for database tests increased from 2.3s to 3.2s over the past week. Correlation with recent database migration.
Check database indexes or optimize recent queries added in migration #456
One memory of failures, every role consumes it
QA learns once. Engineering Leads see the trend. Developers' AI agents get the fix grounded in real history.
Prompt
“Why did checkout.spec.ts flake 8 times this sprint?”
Pattern report: OAuth redirect timing — confidence 92%. Linked sessions + recommended explicit wait.
Prompt
“What broke after deploy #1234?”
Regression alert: 5 payment tests, Stripe SDK version mismatch, confidence 98%. Direct link to commit + rollback flow.
Prompt
“Fix the failing checkout test”
Cursor reads tr_failure_patterns over MCP — proposes a patch grounded in the actual error pattern, not a guess.
Prompt
“Show pattern anomalies across all teams' suites”
Cross-team view of cascading failures and shared regressions — caught before they fan out to every engineer's AI.
Start getting AI insights today
AI Insights are included in the Growth plan. Start your 14-day free trial — no credit card required.