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This paper introduces a cascaded code editing approach that leverages both large and small language models to improve efficiency and effectiveness. A large model first generates concise edit sketches representing the required code modifications, and then a smaller model integrates these sketches into the original code. By offloading the simpler task of sketch application to a smaller model, the approach reduces the computational burden on the large model and improves overall efficiency.
LLMs can be made far more efficient at code editing by having them focus on generating concise "edit sketches," while smaller models handle the less demanding task of applying those sketches to the original code.
Code editing constitutes a fundamental practice in software development, wherein developers modify existing codebases according to natural language requirements. Accurate code editing necessitates a comprehensive understanding of both the existing codebase and the modification requirements. Although large language models (LLMs) have demonstrated promising performance in code editing tasks, they suffer from substantial inefficiency by generating entire modified files that largely consist of unchanged code. While smaller models could potentially address this inefficiency, they typically lack the capacity to effectively comprehend long code contexts required for accurate editing. To ensure both effectiveness and efficiency, we propose to decompose code editing into a two-stage cascade: \textbf{edit sketch generation}, wherein a large model first produces concise sketches representing the requisite modifications (the more challenging phase), and \textbf{edit sketch application}, wherein a smaller model integrates these sketches into the original code to produce the final output edited code (the simpler phase). This cascaded design reduces the number of tokens generated by the large model, as the majority of the output is handled by the smaller, more efficient model, thereby enhancing overall efficiency. However, the effectiveness of this approach is constrained by current small models'limited capabilities in handling long-context scenarios and cross-file dependencies, which are essential for accurate sketch application in real-world codebases. To address these limitations and enhance smaller models'sketch application capabilities, ...
