compare-test-runs
Compares two test runs to identify new failures, newly flaky tests, fixed tests, and duration regressions. Can be invoked with test run IDs, dashboard URLs, or branch names.
What this skill does
# Compare Test Runs ## Quick Start You'll typically receive two test run identifiers. Follow these steps: 1. Run `tuist test show <id> --json` for both base and head test runs. 2. Run `tuist test module list <test-run-id> --json` and `tuist test suite list <test-run-id> --json` to get module and suite breakdowns. 3. Run `tuist test case run list <identifier> --json` to get individual test case results. 4. Compare failures, flaky tests, durations, and overall status. 5. Inspect failing test cases with `tuist test case run show <id> --json`. 6. Summarize findings with actionable recommendations. ## Step 1: Resolve Test Runs ### If base/head are test run IDs or dashboard URLs Fetch each directly: ```bash tuist test show <base-id> --json tuist test show <head-id> --json ``` ### If base/head are branch names List recent test runs on each branch to identify test run IDs: ```bash tuist test list --git-branch <base-branch> --json --page-size 5 tuist test list --git-branch <head-branch> --json --page-size 5 ``` Pick the latest test run ID from each branch's results. ### Defaults - If no base is provided, use the project's default branch (usually `main`). - If no head is provided, detect the current git branch. ## Step 2: Compare Top-Level Metrics After fetching both test runs, compare: | Metric | What to check | |---|---| | `status` | Flag if base passed but head failed | | `duration` | Flag if head is >10% slower | | `total_test_count` | Note if test count changed (new or removed tests) | | `failed_test_count` | Compare failure counts | | `flaky_test_count` | Compare flaky counts | | `avg_test_duration` | Flag significant changes | ## Step 3: Get Module and Suite Breakdowns Fetch module and suite-level results for both test runs to understand which areas regressed: ```bash tuist test module list <base-test-run-id> --json tuist test module list <head-test-run-id> --json tuist test suite list <base-test-run-id> --json tuist test suite list <head-test-run-id> --json ``` Match modules and suites by name across both runs to identify areas with new failures or duration regressions. ## Step 4: Get Individual Test Case Results Fetch test case runs for both test runs: ```bash tuist test case run list <identifier> --json --page-size 100 ``` Match test cases by their `name` + `module_name` + `suite_name` across both runs. ## Step 5: Classify Changes Group test cases into categories: 1. **New failures**: Tests that passed in base but failed in head. 2. **Fixed tests**: Tests that failed in base but passed in head. 3. **Newly flaky**: Tests not flaky in base but flaky in head. 4. **No longer flaky**: Tests that were flaky in base but stable in head. 5. **New tests**: Tests present in head but not in base. 6. **Removed tests**: Tests present in base but not in head. 7. **Duration regressions**: Tests with >50% duration increase. ## Step 6: Inspect Failures For each new failure, get detailed information: ```bash tuist test case run show <test-case-run-id> --json ``` Key fields to 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` -- if present, shows retry behavior (flaky detection) - `crash_report` -- crash data if test runner crashed ## Step 7: Inspect Attachments The `tuist test case run show` output includes attachment and crash report information. Review: - Screenshots or UI test artifacts - Log files or crash reports - Any diagnostic data attached to failing runs ## Summary Format Produce a summary with: 1. **Overall verdict**: Better, worse, or neutral compared to base. 2. **New failures**: List each with failure message, file path, and line number. 3. **New flaky tests**: List with flakiness context. 4. **Fixed tests**: List tests that are now passing. 5. **Duration**: Overall and notable per-test regressions. 6. **Recommendations**: Actionable next steps for each issue. Example: ``` Test Run Comparison: base (run-123 on main) vs head (run-456 on feature-x) Status: success -> failure -- REGRESSION Duration: 120.5s -> 145.2s (+21%) Tests: 342 -> 345 (3 new tests) Failures: 0 -> 2 (2 new failures) Flaky: 1 -> 3 (2 newly flaky) New Failures: 1. AuthModuleTests/LoginTests/test_login_with_expired_token Message: "Expected status 401, got 500" File: Tests/AuthModuleTests/LoginTests.swift:42 Likely cause: Server error handling changed for expired tokens 2. NetworkTests/RetryTests/test_retry_on_timeout Message: "Timed out waiting for retry" File: Tests/NetworkTests/RetryTests.swift:87 Likely cause: Timeout threshold too low after network layer refactor Newly Flaky: 1. CacheTests/WriteCacheTests/test_concurrent_writes (flaky in 3/5 runs) Recommendations: - Fix expired token handling in AuthModule - Increase timeout in RetryTests or mock the network layer - Investigate concurrent write synchronization in CacheTests ``` ## Done Checklist - Resolved both base and head test runs - Compared top-level metrics - Fetched module and suite breakdowns for both runs - Identified new failures, fixed tests, and flaky changes - Inspected failure details for new failures - Provided actionable recommendations with file paths
Related in Data & Analytics
clawarr-suite
IncludedComprehensive management for self-hosted media stacks (Sonarr, Radarr, Lidarr, Readarr, Prowlarr, Bazarr, Overseerr, Plex, Tautulli, SABnzbd, Recyclarr, Unpackerr, Notifiarr, Maintainerr, Kometa, FlareSolverr). Deep library exploration, analytics, dashboard generation, content management, request handling, subtitle management, indexer control, download monitoring, quality profile sync, library cleanup automation, notification routing, collection/overlay management, and media tracker integration (Trakt, Letterboxd, Simkl).
querying-soql
IncludedSOQL query generation, optimization, and analysis with 100-point scoring. Use this skill when the user needs SOQL/SOSL authoring or optimization: natural-language-to-query generation, relationship queries, aggregates, query-plan analysis, and performance or safety improvements for Salesforce queries. TRIGGER when: user writes, optimizes, or debugs SOQL/SOSL queries, touches .soql files, or asks about relationship queries, aggregates, or query performance. DO NOT TRIGGER when: bulk data operations (use handling-sf-data), Apex DML logic (use generating-apex), or report/dashboard queries.
app-store-optimization
IncludedApp Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.
habit-flow
IncludedAI-powered atomic habit tracker with natural language logging, streak tracking, smart reminders, and coaching. Use for creating habits, logging completions naturally ("I meditated today"), viewing progress, and getting personalized coaching.
app-store-optimization
IncludedApp Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.
visualizing-data
IncludedBuilds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.