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

• URL: http://arxiv.org/abs/2602.19407v1
• Date: Mon, 23 Feb 2026 00:54:59 GMT
• Title: Multi-CoLoR: Context-Aware Localization and Reasoning across Multi-Language Codebases
• Authors: Indira Vats, Sanjukta De, Subhayan Roy, Saurabh Bodhe, Lejin Varghese, Max Kiehn, Yonas Bedasso, Marsha Chechik,
• Abstract summary: We present Multi-CoLoR, a framework for Context-aware localization and reasoning across Multi-Languages.<n>It integrates organizational knowledge retrieval with graph-based reasoning to traverse complex software ecosystems.
• Score: 1.4216413758677147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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