AutoCodeRover: Autonomous Program Improvement

Researchers have made significant progress in automating the software development process in the past decades. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless software engineering involves the process of program improvement apart from coding, specifically to enable software maintenance (e.g. bug fixing) and software evolution (e.g. feature additions). In this paper, we propose an automated approach for solving GitHub issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. In contrast to recent LLM agent approaches from AI researchers and practitioners, our outlook is more software engineering oriented. We work on a program representation (abstract syntax tree) as opposed to viewing a software project as a mere collection of files. Our code search exploits the program structure in the form of classes/methods to enhance LLM's understanding of the issue's root cause, and effectively retrieve a context via iterative search. The use of spectrum based fault localization using tests, further sharpens the context, as long as a test-suite is available. Experiments on SWE-bench-lite which consists of 300 real-life GitHub issues show increased efficacy in solving GitHub issues (22-23% on SWE-bench-lite). On the full SWE-bench consisting of 2294 GitHub issues, AutoCodeRover solved around 16% of issues, which is higher than the efficacy of the recently reported AI software engineer Devin from Cognition Labs, while taking time comparable to Devin. We posit that our workflow enables autonomous software engineering, where, in future, auto-generated code from LLMs can be autonomously improved.