Multi-CoLoR: Context-Aware Localization and Reasoning across Multi-Language Codebases

TL;DR

Multi-CoLoR is a novel framework that enhances code localization in multi-language repositories by combining organizational knowledge retrieval with graph-based reasoning, significantly improving accuracy and efficiency.

Contribution

It introduces a two-stage approach integrating issue context retrieval with graph traversal for better multi-language code localization.

Findings

Improves localization accuracy over baseline methods.

Reduces search space and tool calls in code retrieval.

Effective across diverse programming languages.

Abstract

Large language models demonstrate strong capabilities in code generation but struggle to navigate complex, multi-language repositories to locate relevant code. Effective code localization requires understanding both organizational context (e.g., historical issue-fix patterns) and structural relationships within heterogeneous codebases. Existing methods either (i) focus narrowly on single-language benchmarks, (ii) retrieve code across languages via shallow textual similarity, or (iii) assume no prior context. We present Multi-CoLoR, a framework for Context-aware Localization and Reasoning across Multi-Language codebases, which integrates organizational knowledge retrieval with graph-based reasoning to traverse complex software ecosystems. Multi-CoLoR operates in two stages: (i) a similar issue context (SIC) module retrieves semantically and organizationally related historical issues to prune the search space, and (ii) a code graph traversal agent (an extended version of LocAgent, a state-of-the-art localization framework) performs structural reasoning within C++ and QML codebases. Evaluations on a real-world enterprise dataset show that incorporating SIC reduces the search space and improves localization accuracy, and graph-based reasoning generalizes effectively beyond Python-only repositories. Combined, Multi-CoLoR improves Acc@5 over both lexical and graph-based baselines while reducing tool calls on an AMD codebase.