Engineering Dashboard

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Lines of Code

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Source Files

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Repositories

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Risk Flags

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Health Score

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TODOs

Language Distribution

Technology Stack

LOC per Repository

Code Health

Maintainability Score (0–100)

Bus Factor (lower = riskier)

Top Churn Hotspots ▼

Commit Activity

Commit Velocity

Repository Age & Last Activity ▼

Repository	Team	First Commit	Last Commit	Last Active	Age	Total Commits	Commits/mo

Trends

Not enough history yet.

Run the analysis at least twice to see trend charts.
Use run-analysis.ps1 on a weekly schedule to build up data automatically.

Maintainability Score over time

Risk Flags over time

LOC over time

Trend Summary

Repository↕	Team↕	LOC↕	Languages	Tests/Proj	Bus Factor↕	Commits/mo↕	Risk	Score↕	PR Cycle↕	Last Commit↕

Pull Request Metrics

No Azure DevOps PR data available.

Run the analysis with --ado-url and --ado-token to fetch PR cycle time metrics.

Median PR Cycle Time by Team (hours)

PR Throughput by Team (PRs/week)

First Review Time by Team (hours)

Average Reviewers by Team

PR Cycle Time Trend by Team (median hours/month)

PR Throughput Trend by Team (PRs/month)

PR Cycle Time per Repository

PR Metrics Detail ▼

Repository	Team	PRs	PRs/wk	Cycle Avg (h)	Cycle Median (h)	Cycle P90 (h)	1st Review Avg (h)	1st Review Median (h)	Avg Reviewers

AI Usage

No AI-assistance data available for any repository.

Enable by running python analyze.py without --no-ai-assist.

AI-assisted Rate by Team

AI-assisted Rate Trend by Team (monthly, attributed)

AI Usage by Tool per Team (commit-level)

PR vs Commit AI Rate per Repository (top 20 by commit rate)

AI-assisted Rate Trend (workspace total, monthly)

AI Usage Detail ▼

Repository	Team	PR	Commit attr.	Gap pp	Merge strat.	Dominant tool	Tooling	AI / total

DORA Metrics

No DORA metrics available.

Run the analysis with --ado-url and --ado-token to collect pipeline data.
Repos need ADO build pipelines linked to generate deployment metrics.

Deploy pipeline metrics (Lead Time, Deploy Frequency, CFR, MTTR) require --ado-deep. Run python analyze.py --ado-url … --ado-token … --ado-deep to populate these charts. PR-based metrics (Rework Iterations, First Review Time) are shown below from current data.

Lead Time for Changes by Team (hours)

Deploy Frequency by Team (per week)

Change Failure Rate by Team (%)

Mean Time to Recovery by Team (hours)

DORA Trends (monthly)

Avg Rework Iterations per PR

DORA Metrics per Repository ▼

Repository	Team	Deploys/wk	Failure Rate	MTTR (h)	Lead Time (h)	Rework Iters	DORA Rating

DORA ratings based on dora.dev benchmarks. Elite: on-demand deploys, <1h lead time, <5% failure, <1h recovery. Low: <monthly deploys, >1wk lead time, >15% failure, >1wk recovery.

Risk Flags

Risk Distribution

Repos by Risk

Best Practices

Complexity Density (tokens / LOC — lower is better)

Comment Ratio (% of lines)

.NET Platform Mix

Best Practices Scorecard ▼

Repository	Team	Complexity Density	Comment Ratio	Test Ratio	Dependencies	Avg File Size	.NET Frameworks	BP Grade

Complexity Density: control-flow tokens ÷ LOC (0.05–0.15 is healthy, >0.25 is concerning). | Comment Ratio: comment lines ÷ total lines (>10% is healthy). | BP Grade: A–F composite across all four signals.

Dependencies

NuGet Packages

Shared only

Hide framework

Inconsistent versions

npm Packages

Shared only

Inconsistent versions

Service Map

Shared-Dependency Service Map

Min shared packages: 5

Nodes sized by LOC & coloured by team. Edge thickness = shared NuGet packages. Drag nodes · Scroll to zoom · Click a node to highlight its connections · use the slider to hide weak links.

Customers Modules

customers — Internal Module Map (cross-module ProjectReference dependencies)

Module Statistics

Nodes sized by C# LOC. Arrows show which module references the other via ProjectReference. Drag nodes · Scroll to zoom · Click a node to highlight its connections.

Code Health

Commit Activity

Trends

Pull Request Metrics Monthly Sprint (2wk)

AI Usage

DORA Metrics

Risk Flags

Best Practices

Dependencies

Service Map

Customers Modules

Pull Request Metrics