RepoScope — AI-Powered Repository Intelligence

Modern software repositories can contain hundreds or thousands of files, functions, classes, and interconnected modules. When developers join an existing project or investigate unfamiliar code, finding the right information often requires manually searching across files and tracing imports and dependencies. Traditional keyword-based search can locate matching text, but it does not inherently explain how code components are connected or which related files are important to the question. This makes repository onboarding, dependency investigation, debugging, and understanding unfamiliar systems time-consuming. Developers also need confidence in AI-generated explanations. An answer without clear evidence or source locations can be difficult to verify, especially when reasoning about a large codebase. The challenge is therefore to provide a practical way to understand a repository through both its textual content and its underlying code relationships, while keeping AI responses grounded in identifiable repository evidence. Solution Statement RepoScope addresses this problem by building a structured intelligence layer over a software repository. The system first ingests repository files and extracts structural information such as classes, functions, methods, modules, signatures, and line ranges. It then identifies relationships between files, including IMPORTS and EXPORTS, and indexes repository content into searchable chunks. When a developer asks a question, RepoScope combines lexical retrieval with relationship-aware expansion to identify relevant files, components, and connected code. A context builder assembles the retrieved evidence, including source locations and relationship information, and passes the grounded context to IBM watsonx.ai and Granite to generate an answer. The resulting response is accompanied by source files, line ranges, and relationship evidence, allowing developers to trace the explanation back to the repository. A dependency graph further provides a visual representation of how relevant code components are connected. This approach combines repository structure, relationships, retrieval, and generative AI into a single workflow for exploring and understanding unfamiliar codebases.