Can I use this for sprint reviews?

Yes, it includes a dashboard generator that provides an executive summary of quality trends, making it ideal for sprint retrospectives and reviews.

What specific metrics does this skill track?

The skill tracks Regression Rate, Mean Time to Detect (MTTD), Mean Time to Fix (MTTF), Escape Rate (production bugs), and Recurrence Rate.

How does it identify high-risk code areas?

It generates component heatmaps by correlating historical regression data with specific directories and files to highlight where bugs occur most frequently.

Where does the skill gather its data?

It aggregates data from regression test results, bisect reports, baseline comparisons, issue trackers (like GitHub Issues), and CI/CD logs.

Regression Metrics & Analytics

Name: Regression Metrics & Analytics
Author: jmagly

byjmagly

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102

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Analytics & Monitoring

Monitors and analyzes regression statistics to track software health, identify hotspots, and improve detection speed.

This skill provides a comprehensive analytical layer for software quality by tracking key performance indicators such as regression rates, Mean Time to Detect (MTTD), and Mean Time to Fix (MTTF). By aggregating data from test results, issue trackers, and CI/CD logs, it generates actionable dashboards and heatmaps that pinpoint high-risk components and production escape patterns. It is essential for engineering teams aiming to transition from reactive bug fixing to proactive quality management through data-driven insights and multi-sprint trend analysis.

Key Features

01Component-level heatmaps to identify high-risk code areas

02Dynamic regression health dashboards with executive summaries

03Historical trend analysis across multiple sprints or releases

04Automated calculation of MTTD, MTTF, and Regression Escape Rates

05102 GitHub stars

06Actionable process recommendations based on root cause patterns

Use Cases

01Identifying specific modules that require better integration test coverage

02Measuring the impact of new automation tools on regression detection speed

03Generating weekly quality reports for engineering leadership and stakeholders

Key Features

01Component-level heatmaps to identify high-risk code areas

02Dynamic regression health dashboards with executive summaries

03Historical trend analysis across multiple sprints or releases

04Automated calculation of MTTD, MTTF, and Regression Escape Rates

05102 GitHub stars

06Actionable process recommendations based on root cause patterns

Use Cases

01Identifying specific modules that require better integration test coverage

02Measuring the impact of new automation tools on regression detection speed

03Generating weekly quality reports for engineering leadership and stakeholders