Codebase Time Machine

Codebase Time Machine helps developers understand not only what a codebase is today, but how it got there. Problem: When developers join an unfamiliar project, the current source code shows what exists, but much of the historical context is hidden inside Git commits, diffs, bug fixes, refactoring, and architectural changes. Manually reconstructing this history can be time-consuming and difficult, especially during developer onboarding. Solution: Codebase Time Machine extracts structured Git history and transforms it into an evidence-grounded historical analysis. The workflow identifies important commits, groups related changes into historical epochs, analyzes significant changes, reconstructs architectural evolution, and produces a developer onboarding guide. The project uses a controlled TaskFlow Python repository containing 10 meaningful commits as its demonstration dataset. The system extracts commit metadata, changed files, line changes, and diff snippets into structured JSON. IBM Bob then analyzes this evidence to produce historical epochs, "Why did this change?" analyses, onboarding guidance, and a self-contained HTML evolution report. Target Users: The primary users are software developers, engineering teams, technical leads, and developers onboarding onto unfamiliar repositories. User Interaction: A developer provides or analyzes a local Git repository. The extraction layer generates structured repository history. The resulting analysis is presented through the generated evolution_report.html, which contains a repository snapshot, historical timeline, historical epochs, change explanations, architecture information, known issues, and onboarding guidance. Creative and Unique Aspect: Instead of treating Git as only a version-control system, Codebase Time Machine treats Git history as a source of historical context. It separates observed repository evidence from inference and explicitly records uncertainty when the repository cannot establish why a change happened. The result is a "time machine" for a codebase: developers can understand the sequence of changes that produced the current architecture instead of relying only on the final state of the source code.