On the Impacts of Contexts on Repository-Level Code Generation

• URL: http://arxiv.org/abs/2406.11927v4
• Date: Sun, 09 Feb 2025 08:47:20 GMT
• Title: On the Impacts of Contexts on Repository-Level Code Generation
• Authors: Nam Le Hai, Dung Manh Nguyen, Nghi D. Q. Bui,
• Abstract summary: We present RepoExec, a novel benchmark designed to evaluate repository-level code generation. We focus on three key aspects: executability, functional correctness through comprehensive test case generation, and accurate utilization of cross-file contexts.
• Score: 5.641402231731082
• License:
• Abstract: CodeLLMs have gained widespread adoption for code generation tasks, yet their capacity to handle repository-level code generation with complex contextual dependencies remains underexplored. Our work underscores the critical importance of leveraging repository-level contexts to generate executable and functionally correct code. We present RepoExec, a novel benchmark designed to evaluate repository-level code generation, with a focus on three key aspects: executability, functional correctness through comprehensive test case generation, and accurate utilization of cross-file contexts. Our study examines a controlled scenario where developers specify essential code dependencies (contexts), challenging models to integrate them effectively. Additionally, we introduce an instruction-tuned dataset that enhances CodeLLMs' ability to leverage dependencies, along with a new metric, Dependency Invocation Rate (DIR), to quantify context utilization. Experimental results reveal that while pretrained LLMs demonstrate superior performance in terms of correctness, instruction-tuned models excel in context utilization and debugging capabilities. RepoExec offers a comprehensive evaluation framework for assessing code functionality and alignment with developer intent, thereby advancing the development of more reliable CodeLLMs for real-world applications. The dataset and source code are available at https://github.com/FSoft-AI4Code/RepoExec.

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