# A Task-Level Evaluation of AI Agents in Open-Source Projects

arXiv: https://arxiv.org/abs/2602.02345 · HTML: https://mlsanity.com/paper/2602.02345/

## Key facts

- Cited by 2 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.
- First posted on arXiv 2 Feb 2026.

## Placed in

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

## Built on by (most cited first, 2 of 2)

- [Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles](https://mlsanity.com/paper/2605.08017/) (2026)
- [3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse](https://mlsanity.com/paper/2607.07980/) (2026)

## Abstract (arXiv)

In this paper, we present a comparative study of five autonomous coding agents using AIDev-pop, which is a public dataset containing thousands of AI-generated pull requests (PRs) across popular open-source repositories. We evaluate agents' performance along three task-aware dimensions spanning the PR lifecycle: (1) PR acceptance rate, (2) review discussion volume, and (3) commit message quality. Our quantitative analysis finds that Codex consistently achieves high PR acceptance rates across most task categories, while Copilot's PRs trigger the highest volume of both human and automated review discussions. In contrast, commit-level quality varies independently of acceptance outcomes. Claude and Cursor produce higher proportions of high-quality commit messages across several task types, and Codex exhibiting comparatively lower commit quality despite strong integration outcomes. Our findings inform selection and improvements of AI agents for their effective integration to collaborative software engineering.