Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
3-2025
Abstract
In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single agent dominated, with 170 issues unresolved, indicating room for improvement. Even for patches that passed unit tests and resolved issues, agents made different file and function modifications compared to the gold patches from repository developers, revealing limitations in the benchmark's test case coverage. Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities; while some agents increased code complexity, many reduced code duplication and minimized code smells. Finally, agents performed better on simpler codebases, suggesting that breaking complex tasks into smaller sub-tasks could improve effectiveness. This study provides the first comprehensive evaluation of agent-generated patches on real-world GitHub issues, offering insights to advance AI-driven software development.
Keywords
Software Development Agents, Patch Generation, Large Language Models, Code Quality, GitHub Issues
Discipline
Artificial Intelligence and Robotics | Software Engineering
Areas of Excellence
Digital transformation
Publication
Proceedings of the 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Montreal, Canada, March 4-7
First Page
657
Last Page
668
Identifier
10.1109/SANER64311.2025.00068
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
CHEN, Zhi and JIANG, Lingxiao.
Evaluating software development agents: Patch patterns, code quality, and issue complexity in real-world GitHub scenarios. (2025). Proceedings of the 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Montreal, Canada, March 4-7. 657-668.
Available at: https://ink.library.smu.edu.sg/sis_research/10767
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/SANER64311.2025.00068