mlsanityData to 7 Oct 2026

Programming with Pixels: Can Computer-Use Agents do Software Engineering?

arXiv 2502.18525 ↗First posted 24 Feb 2025cs.SE cs.LGcited by 4

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Computer-use agents (CUAs) hold the promise of performing a wide variety of general tasks, but current evaluations have primarily focused on simple scenarios. It therefore remains unclear whether such generalist agents can automate more sophisticated and specialized work such as software engineering (SWE). To investigate this, we introduce textttProgramming with Pixels (PwP), the first comprehensive computer-use environment for software engineering, where agents visually control an IDE to perform diverse software engineering tasks. To enable holistic evaluation, we also introduce textttPwP-Bench, a benchmark of 15 existing and new software-engineering tasks spanning multiple modalities, programming languages, and skillsets. We perform an extensive evaluation of state-of-the-art open-weight and closed-weight CUAs and find that when interacting purely visually, they perform significantly worse than specialized coding agents. However, when the same CUAs are given direct access to just two APIs-file editing and bash operations-performance jumps, often reaching the levels of specialized agents despite having a task-agnostic design. Furthermore, when given access to additional IDE tools via text APIs, all models show further gains. Our analysis shows that current CUAs fall short mainly due to limited visual grounding and the inability to take full advantage of the rich environment, leaving clear room for future improvements.PwP establishes software engineering as a natural domain for benchmarking whether generalist computer-use agents can reach specialist-level performance on sophisticated tasks. Code and data released at https://programmingwithpixels.com

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 26 of its 44 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

26 of 44 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks20 Jan
  2. 2024From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future5 Aug
  3. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
  4. 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents18 Jun
  5. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
  6. 2024Executable Code Actions Elicit Better LLM Agents1 Feb

Other references · 20

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  4. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  5. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
  6. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  7. 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 547
  8. 2024Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agentscited by 373
  9. 2023Android in the Wild: A Large-Scale Dataset for Android Device Controlcited by 314
  10. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Taskscited by 306
  11. 2023Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4Vcited by 291
  12. 2023InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedbackcited by 229
8 more
  1. 2024SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?cited by 196
  2. 2024Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineeringcited by 146
  3. 2024DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?cited by 119
  4. 2024ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generationcited by 118
  5. 2023Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructionscited by 46
  6. 2024miniCTX: Neural Theorem Proving with (Long-)Contextscited by 35
  7. 2024SWE-bench-java: A GitHub Issue Resolving Benchmark for Javacited by 34
  8. 2024RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scalecited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2023Autonomous Agents in Software Development: A Vision Paper
  2. 2025A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks
  3. 2025Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases
  4. 2026SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
  5. 2025Computer-Use Agents as Judges for Generative User Interface
  6. 2026SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
  7. 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
  8. 2025SeaView: Software Engineering Agent Visual Interface for Enhanced Workflow