Unified Software Engineering Agent as AI Software Engineer
Leonhard Applis, Yuntong Zhang, Shanchao Liang, Nan Jiang, Lin Tan, Abhik Roychoudhury
Abstract
The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities including maintenance and evolution of a project. In this context, the concept of LLM agents has gained traction, which utilize LLMs as reasoning engines to invoke external tools autonomously. But is an LLM agent the same as an AI software engineer? In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent. Unlike existing work which builds specialized agents for specific software tasks such as testing, debugging, and repair, our goal is to build a unified agent which can orchestrate and handle multiple capabilities. This gives the agent the promise of handling complex scenarios in software development such as fixing an incomplete patch, adding new features, or taking over code written by others. We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans. To evaluate the efficacy of USEagent, we build a Unified Software Engineering bench (USEbench) comprising of myriad tasks such as coding, testing, and patching. USEbench is a judicious mixture of tasks from existing benchmarks such as SWE-bench, SWT-bench, and REPOCOD. In an evaluation on USEbench consisting of 1,271 repository-level software engineering tasks, USEagent shows improved efficacy compared to existing general agents such as OpenHands CodeActAgent. For specific tasks such as issue resolution in SWE-bench, USEagent demonstrates efficacy comparable to agents such as AutoCodeRover (which are focused on software maintenance), while maintaining applicability to a broader range of software engineering tasks. There exist gaps in the capabilities of USEagent for certain coding tasks, which provides hints on further developing the AI Software Engineer of the future.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers5
- Structurally Aligned Subtask-Level Memory for Software Engineering AgentsKangning Shen, Jingyuan Zhang, Chenxi Sun, Wencong Zeng et al.ICML 2026 · 9 citations
- TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test GenerationSteven Liu, Jane Luo, Xin Zhang, Aofan Liu et al.ICML 2026 · 4 citations
- Heterogeneous Prompting and Execution Feedback for SWE Issue Test Generation and SelectionToufique Ahmed, Jatin Ganhotra, Avraham Shinnar, Martin HirzelICSE 2026 · 2 citations
- AutoCodeSherpa: Symbolic Explanations in AI Coding AgentsSungmin Kang, Haifeng Ruan, Abhik RoychoudhuryISSTA 2026
- Can Old Tests Do New Tricks for Resolving SWE Issues?Yang Chen, Toufique Ahmed, Reyhaneh Jabbarvand, Martin HirzelFSE 2026
Builds on14
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code GenerationJiawei Liu, Chunqiu Steven Xia, Yuyao Wang, Lingming ZhangNeurIPS 2023 · 2,317 citations
- SWE-bench: Can Language Models Resolve Real-world Github Issues?Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao et al.ICLR 2024 · 2,082 citations
- SWE-agent: Agent-Computer Interfaces Enable Automated Software EngineeringJohn Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret et al.NeurIPS 2024 · 2,059 citations
- Executable Code Actions Elicit Better LLM AgentsXingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang et al.ICML 2024 · 436 citations
- SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code AgentsNiels Mündler, Mark Niklas Müller, Jingxuan He, Martin T. VechevNeurIPS 2024 · 172 citations
Related papers
- Can Agent Fix Agent Issues?Alfin Wijaya Rahardja, Junwei Liu, Weitong Chen, Zhenpeng Chen et al.NeurIPS 2025 · 4 citations
- AutoCodeRover: Autonomous Program ImprovementYuntong Zhang, Haifeng Ruan, Zhiyu Fan, Abhik RoychoudhuryISSTA 2024 · 96 citations
- SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language ModelsJingxuan Xu, Ken Deng, Weihao Li, Songwei Yu et al.ICML 2026 · 9 citations
- RepoGraph: Enhancing AI Software Engineering with Repository-level Code GraphSiru Ouyang, Wenhao Yu, Kaixin Ma, Zilin Xiao et al.ICLR 2025
- CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding ChallengesKechi Zhang, Jia Li, Ge Li, Xianjie Shi et al.ACL 2024
