# Unified Software Engineering Agent as AI Software Engineer

arXiv: https://arxiv.org/abs/2506.14683 · HTML: https://mlsanity.com/paper/2506.14683/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 45 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 22 of 22)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation](https://mlsanity.com/paper/2308.01861/) (2023)
- [SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents](https://mlsanity.com/paper/2406.12952/) (2024)
- [SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement](https://mlsanity.com/paper/2410.20285/) (2024)
- [CodeR: Issue Resolving with Multi-Agent and Task Graphs](https://mlsanity.com/paper/2406.01304/) (2024)
- [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024)
- [You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects](https://mlsanity.com/paper/2412.10133/) (2024)
- [HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation](https://mlsanity.com/paper/2412.21199/) (2024)
- [Evaluating Agent-based Program Repair at Google](https://mlsanity.com/paper/2501.07531/) (2025)
- [AutoDev: Automated AI-Driven Development](https://mlsanity.com/paper/2403.08299/) (2024)
- [Can Language Models Replace Programmers for Coding? REPOCOD Says 'Not Yet'](https://mlsanity.com/paper/2410.21647/) (2024)

## Built on by (most cited first, 19 of 19)

- [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025)
- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [Structurally Aligned Subtask-Level Memory for Software Engineering Agents](https://mlsanity.com/paper/2602.21611/) (2026)
- [The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering](https://mlsanity.com/paper/2507.15003/) (2025)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation](https://mlsanity.com/paper/2602.10471/) (2026)
- [Past, Present, and Future of Bug Tracking in the Generative AI Era](https://mlsanity.com/paper/2510.08005/) (2025)
- [Toward Agentic Software Engineering Beyond Code: Framing Vision, Values, and Vocabulary](https://mlsanity.com/paper/2510.19692/) (2025)
- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation](https://mlsanity.com/paper/2510.00591/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [AutoCodeSherpa: Symbolic Explanations in AI Coding Agents](https://mlsanity.com/paper/2507.22414/) (2025)
- [Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents](https://mlsanity.com/paper/2601.10820/) (2026)
- [What Do Agents Communicate? Characterizing Information Exchange in Multi-Agent Systems](https://mlsanity.com/paper/2605.20548/) (2026)
- [Do programming languages still matter to your AI coding agent teammate? Evidence at scale from chess engines](https://mlsanity.com/paper/2606.13763/) (2026)
- [How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling](https://mlsanity.com/paper/2604.04791/) (2026)
- [CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly](https://mlsanity.com/paper/2605.26195/) (2026)
- [Benchmarks are Not Enough: RAMP for Runtime Assessing of Agentic Models in Production Systems](https://mlsanity.com/paper/2605.27492/) (2026)
- [ExplainBench: Evaluating Code Explanations from Agents](https://mlsanity.com/paper/2607.26451/) (2026)

## Abstract (arXiv)

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. 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.