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compare-test-case

compare-test-case

github.com/tuistTesting & quality35238 installsUpdated 31w ago
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About compare-test-case

A skill for comparing one test case across two branches with the Tuist CLI, for developers investigating failures, flakiness, or duration changes. It uses pass rates, run times, and failure details to support root-cause analysis and recommendations.

Key features

  1. 01Resolve cases by ID or name
  2. 02Group runs by branch
  3. 03Compare pass rates, duration, and flakiness
  4. 04Inspect failure messages and crash reports
  5. 05Summarize causes and fix recommendations

Use cases

  1. 01Investigate a case failing on a new branch
  2. 02Check whether a test has become flaky
  3. 03Track a single case’s duration regression
  4. 04Use failure locations to guide fixes

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tuist/agent-skills --skill compare-test-case

After installing, type/compare-test-casein Claude Code to confirm it loaded.

Details

From the project README

Compare Test Case

Quick Start

You'll typically receive a test case identifier and two branches. Follow these steps:

  1. Run tuist test case show <id-or-identifier> --json to get the test case metrics.
  2. Run tuist test case run list <identifier> --json to see runs across branches.
  3. Compare behavior between base and head branches.
  4. Inspect failures with tuist test case run show <run-id> --json.
  5. Summarize findings with root cause analysis.

Step 1: Resolve the Test Case

By ID or dashboard URL
tuist test case show <test-case-id> --json
By identifier (Module/Suite/TestCase)
tuist test case show Module/Suite/TestCase --json
If no test case is provided

Discover flaky or failing tests to investigate:

tuist test case list --flaky --json --page-size 10

Key fields from the response:

  • id -- unique identifier for subsequent commands
  • name, module_name, suite_name -- the test identity
  • reliability_rate -- percentage of successful runs
  • flakiness_rate -- percentage of flaky runs in the last 30 days
  • total_runs / failed_runs -- volume context
  • is_flaky / is_quarantined -- current flags

Step 2: Get Runs on Each Branch

List test case runs filtered by the test case, and look at the git_branch field:

tuist test case run list <identifier> --json --page-size 20

Separate runs by branch. For each branch, compute:

  • Pass rate: passed_runs / total_runs * 100
  • Average duration
  • Flaky run count
  • Most recent status
Defaults
  • If no base branch is provided, use the project's default branch (usually main).
  • If no head branch is provided, detect the current git branch.

Step 3: Compare Branch Behavior

MetricBase branchHead branchVerdict
Pass ratee.g. 100%e.g. 60%REGRESSION
Avg duratione.g. 0.5se.g. 2.1sREGRESSION
Flaky runs03NEW FLAKINESS
Last statussuccessfailureREGRESSION

Classify the change:

  • Newly failing: 100% pass rate on base, <100% on head
  • Newly flaky: No flaky runs on base, flaky runs on head
  • Duration regression: >50% increase in average duration
  • Fixed: Failing on base, passing on head
  • Stable: Same behavior on both branches

Step 4: Inspect Failures

For each failing run on the head branch:

tuist test case run show <test-case-run-id> --json

Examine:

  • failures[].message -- the assertion or error message
  • failures[].path -- source file path
  • failures[].line_number -- exact line of failure
  • failures[].issue_type -- type of issue
  • repetitions -- shows retry behavior (e.g., pass-fail-pass means flaky)
  • crash_report -- crash data if the test runner crashed

Step 5: Identify Root Cause

Based on the comparison:

Newly failing
  • Check commits between base and head branches for changes to the test file or the code under test.
  • Look at the failure message for clues about what changed.
Newly flaky
  • Common patterns: timing/async issues, shared state, environment dependencies.
  • Check if repetitions show intermittent pass/fail patterns.
  • See the fix-flaky-tests skill for detailed flaky test analysis patterns.
Duration regression
  • Check if setup/teardown time increased.
  • Check if the test is doing more work (new assertions, larger data sets).
  • Check if a dependency became slower.

Summary Format

Produce a summary with:

  1. Test case info: Name, module, suite, overall reliability.
  2. Base branch behavior: Pass rate, avg duration, flaky count.
  3. Head branch behavior: Pass rate, avg duration, flaky count.
  4. Verdict: What changed and classification.
  5. Root cause: Hypothesis based on failure analysis.
  6. Recommendations: Specific file paths, line numbers, and fix suggestions.

Example:

Test Case Comparison: AuthModuleTests/LoginTests/test_login_with_expired_token

Overall reliability: 85% (was 100% before head branch)

Base (main):
  Pass rate: 100% (15/15 runs)
  Avg duration: 0.3s
  Flaky: No

Head (feature/auth-refactor):
  Pass rate: 60% (3/5 runs)
  Avg duration: 0.5s
  Flaky: Yes (2 flaky runs)

Verdict: NEWLY FLAKY -- test was stable on main but intermittently fails on feature branch

Root cause: The auth refactor introduced an async token refresh that races with the
test's synchronous assertion. Failures show "Expected status 401, got nil" at
Tests/AuthModuleTests/LoginTests.swift:42, suggesting the response arrives before
the token refresh completes.

Recommendations:
- Add an await/expectation before the assertion at LoginTests.swift:42
- Consider mocking the token refresh to make the test deterministic

Done Checklist

  • Resolved the test case identity
  • Gathered runs on both branches
  • Compared pass rates, durations, and flakiness
  • Inspected failure details for failing runs
  • Identified root cause with file paths and line numbers
  • Provided actionable fix recommendations

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