CUA-SWE: When Computer-Use Agents Meet Visual Software Engineering
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Software development requires more than editing code: developers repeatedly run software, interact with its interfaces, visually inspect its behavior, and use these observations to decide what to change next and whether a change works. Existing coding agents and computer-use agents are largely studied in isolation, leaving this integrated development process underexplored. Diagnosing a runtime interaction failure requires agents to connect visual observations with the responsible code, then use the application again to verify the repair. We introduce CUA-SWE, a benchmark, environment, and evaluation pipeline for software engineering with computer use. Beyond studying how GUI feedback supports diagnosis and repair, we ask whether agents can complete software engineering tasks when required specification or operational information is available only through the running application's visual interface. CUA-SWE spans four software engineering domains and requires agents to modify code and configuration, execute commands, interact with running software, and inspect visual feedback within the same task. Each task includes deterministic, task-specific tests that verify whether the resulting software satisfies the requirements and preserves specified behavior. Our evaluation characterizes how frontier agents combine source-level execution with application screenshots and graphical interaction to produce verified software changes. We examine performance across domains and task information requirements, alongside the development behaviors associated with successful repairs. CUA-SWE provides a unified testbed for studying how agents use visual feedback and interaction to guide software engineering, with executable correctness criteria for the resulting software.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 27 of its 30 references that are in the map. First posted on arXiv 26 Sep 2026.
Builds on
27 of 30 references.
Closest ancestors · 6
Same direction, newest first
- 2026GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?16 Jun
- 2026GameDevBench: Evaluating Agentic Capabilities Through Game Development11 Feb
- 2025WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch6 May
- 2025Programming with Pixels: Can Computer-Use Agents do Software Engineering?24 Feb
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
Other references · 21
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
- 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
- 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agentscited by 584
- 2024BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructionscited by 527
- 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
- 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
- 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 424
9 more
- 2025SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?cited by 254
- 2025ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Usecited by 246
- 2024Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scalecited by 212
- 2024SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?cited by 196
- 2024On the Effects of Data Scale on UI Control Agentscited by 187
- 2025Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agentscited by 156
- 2025OpenCUA: Open Foundations for Computer-Use Agentscited by 151
- 2025SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?cited by 99
- 2026WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfacescited by 9
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- 2026SWE-Touch: Benchmarking Coding Agents When Users Touch the Code
- 2025SeaView: Software Engineering Agent Visual Interface for Enhanced Workflow
- 2026Agent-Computer Observation Interfaces Enable Dynamic Computer Use
- 2026Projecting the Emerging Mindset of SWE Agent by Launching a Wild Code Understanding Journey
- 2026AppLooper: An Agentic Application Engineering Loop for Accountable Release with Virtual-User Feedback
- 2025A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks
- 2025Computer-Use Agents as Judges for Generative User Interface