engineering-analytics

An ongoing curated list of awesome frameworks, important books and articles, talks and videos, libraries, learning tutorials, coding best practices and technical resources about Practical Engineering

Git repository archaeology framework — mine commit history, detect signals, run 6 analysis vectors, and generate engineering reports. Python CLI + AI-agent ready.

IME568 - Engineering Analytics Assignments & Coursework

Is your AI coding spend delivering? Offline-first analytics for Claude Code, Codex CLI, Gemini CLI, GitHub Copilot CLI and Cline — $/100 AI lines per project, every verdict carrying its own confidence, a self-contained dashboard, exec plugins in any language, and a self-hosted team server. No telemetry; prompts are never read.

AWS-native delivery intelligence platform that connects Jira and GitLab, diagnoses engineering workflow bottlenecks, runs guarded Text2SQL analytics, and measures intervention ROI.

Local-first GitHub engineering dashboard for syncing team activity, tracking PR risks, and generating stakeholder-ready reports

Artificial intelligence, machine learning, and data driven methods for industrial process engineering.

tenXer was a San Francisco productivity-analytics startup that helped engineering leads measure and improve team performance by pulling data from the tools engineers already used (GitHub, JIRA, Pivotal Tracker, etc.) and turning it into manager-facing views, stats and insights.

FlowBoard generates self-contained HTML delivery dashboards from Jira data, with sprint health, capacity, epic progress, risk detection, blockers, dependencies, and what-if simulations.

LinearB is a software engineering intelligence (SEI) and developer productivity platform that correlates Git, CI/CD, project management, and incident data into DORA and engineering metrics. The LinearB REST API lets teams report deployments, push incidents, export measurements, and manage teams and services programmatically.

Full-stack engineering intelligence platform for PR risk, review bottlenecks, workload balance, hotspot files, technical debt, and release readiness.

Recreated an NLP-based failure classification pipeline inspired by internship work, predicting component failure categories from customer complaint data using machine learning and text analytics.

Code Climate provides automated code review and engineering intelligence for software teams. The Quality REST API (v1) exposes organizations, repositories, maintainability and test-coverage analysis, snapshots, and issues, while Velocity surfaces engineering analytics across the software delivery lifecycle.