SWE-Manager: Selecting and Synthesizing Golden Proposals Before Coding
Boyin Tan, Haoning Deng, Junyuan Zhang, Junjielong Xu, Pinjia He, Youcheng Sun

TL;DR
This paper introduces SWE-Manager, an RL-trained model that selects and synthesizes optimal proposals for software issues, improving decision accuracy and reducing operational risks in software engineering workflows.
Contribution
SWE-Manager is the first model to jointly select and synthesize proposals for software issues using reinforcement learning, inspired by real-world decision-making processes.
Findings
Achieves 53.21% selection accuracy on SWE-Lancer benchmark
Attains 57.75% earn rate, outperforming GPT-5 and other baselines
Demonstrates effectiveness in real-world issue resolution workflows
Abstract
Large language model (LLM) research in software engineering has largely focused on tasks such as code generation and bug repair. In practice, teams often draft multiple candidate proposals for fixing an issue and then deliberate on one golden proposal for implementation. This selection requires not only assessing the issue's scope, impact, and urgency, but also a clear understanding of each proposal's strengths and weaknesses. A good selection could make issue resolution more reliable while reducing regression and operational risk, whereas a poor choice can increase risk and even cause unpredictable failures. We first conduct a manual study of real-world issues to characterize the rationales maintainers use when selecting among competing proposals. Motivated by these findings, we introduce SWE-Manager, a joint selection and synthesis approach that selects the best proposal and…
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Taxonomy
TopicsSoftware Engineering Research · Software Engineering Techniques and Practices · Software Testing and Debugging Techniques
