AI That Understands Your Test Failures

Automatic categorization, pattern detection, and root cause analysis

How AI Analysis Works

Transparent, explainable AI built for QA teams

1

Data Collection

We collect test logs, stack traces, screenshots, and execution metadata from all your test runs

2

Pattern Recognition

Our ML models analyze failures across time, identifying recurring patterns and correlations

3

Root Cause Analysis

AI determines the most likely root cause and provides actionable recommendations

ML Model Transparency

Our AI models are trained on millions of test executions and continuously improved based on user feedback. We provide confidence scores and explanations for every insight.

95% accuracy in failure categorization
90% of root causes identified correctly
60% reduction in debugging time

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

95%
Failure Categorization Accuracy

Correctly identifies the type of failure

90%
Root Cause Accuracy

Pinpoints the actual cause of failure

60%
Time Savings

Average reduction in debugging time

How We Measure Accuracy

We continuously validate our AI models against human-labeled data and user feedback. Our accuracy metrics are updated in real-time and available on our status page.

Sample Insights

See what AI insights look like in practice

Flaky Test Detection
Confidence: High (92%)

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.

Recommendation:

Add explicit wait for OAuth callback or increase timeout threshold

Regression Alert
Confidence: Very High (98%)

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.

Recommendation:

Stripe SDK version mismatch detected. Rollback to previous version or update test mocks.

Performance Degradation
Confidence: Medium (78%)

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.

Recommendation:

Check database indexes or optimize recent queries added in migration #456

Start getting AI insights today

No credit card required. Free tier includes basic AI insights.