FixSense
Features

AI Analysis

How FixSense uses AI to analyze E2E test failures and identify root causes.

Overview

Every time a Playwright or Cypress test fails in your CI, FixSense uses AI to perform intelligent root cause analysis. The AI examines the failure logs, test context, and error patterns to deliver actionable insights.

What Gets Analyzed

For each failed test, the AI receives:

  • Error message and stack trace
  • Test file name and test title
  • Expected vs actual behavior
  • Test framework log output (actions, assertions, network)
  • Screenshot and trace references (if available)

Analysis Output

Each analysis produces a structured result:

Root Cause

A clear explanation of why the test failed. Examples:

  • "The login button selector #submit was changed to .btn-primary in the latest commit"
  • "A race condition: the API response arrives after the assertion timeout of 5000ms"
  • "The test relies on a specific data seed that was modified in the database migration"

Failure Category

Each failure is categorized as:

CategoryDescription
RegressionA code change broke existing functionality
FlakyIntermittent failure due to timing, network, or environment
Test MaintenanceTest code needs updating to match new UI/behavior
EnvironmentCI environment issue (dependency, service, config)

Fix Suggestion

Specific code changes to resolve the issue:

// Before (failing)
await page.click('#submit');

// After (suggested fix)
await page.click('.btn-primary');

Confidence Score

A 0-100% score indicating how certain the AI is about its analysis. Scores above 80% are typically very accurate.

Analysis Actions

Each analysis card in the dashboard has actions you can take:

Reanalyze

Triggers a new AI API call on the same failure data (test name, error message, and diff context). The AI may produce different root cause explanations, flakiness scores, and fix suggestions on each run. Useful when:

  • The original analysis had low confidence
  • An AI error occurred during the first attempt
  • You want a fresh perspective after understanding the failure better

The card shows a spinner while re-analyzing and updates in-place with the new results — root cause, flakiness score, suggested fix, and confidence are all replaced.

Each reanalyze counts as one analysis toward your monthly quota, since it makes a real AI API call. If you've reached your plan limit, the button will return an error.

Copy

Copies the full analysis to your clipboard in a clean text format — test name, root cause, error message, and suggested fix. Useful for pasting into Slack, Jira, or team discussions.

Delete

Permanently removes the analysis from your dashboard and database. Includes an inline confirmation step to prevent accidental deletion.

Pattern Learning

FixSense learns from your feedback to get smarter over time:

  • When you mark an analysis as helpful, FixSense remembers the failure pattern. If the same test fails with the same error again, you get an instant cached result — no waiting, no extra analysis usage.
  • When you mark an analysis as unhelpful, FixSense ensures it won't reuse that result. The next time the same failure occurs, a fresh analysis runs with improved context.

This means the more you use FixSense, the faster and more accurate it becomes for your specific codebase.

Efficiency

FixSense only analyzes failed tests. Passing tests are completely ignored, keeping your usage efficient and your monthly analysis count low.

A typical team with 500 tests uses only a fraction of their monthly analysis quota, even on the Free plan.