Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.
Aymen Masmoudi, Belhassen Khefacha, Andrew Haralambous et al.· Annual International Compute...· 0 citations
Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.
Aymen Masmoudi, Belhassen Khefacha, Andrew Haralambous et al.· Annual International Compute...· 0 citations
The integration of AI-powered coding agents into Continuous Integration/Continuous Delivery (CI/CD) pipelines has fundamentally altered how software verification is conducted. While these agents successfully automate the test generation, current evaluation benchmarks (e.g., SWE-bench) largely focus on pass-rates rather than the intrinsic quality of the generated tests. This raises the possibility of “stealth technical debt”, in which test suites pass execution but do not offer comprehensive coverage or semantic value. We address this methodological gap through a large-scale, empirical comparison of 204,673 test artifacts which comprises of 24,941 human-authored files and 179,732 agent-generated files; sourced from the AIDev dataset. Using the Abstract Syntax Tree (AST) parsing with Python's naive ast module, we implemented a “white-box” static analysis framework to evaluate three quality dimensions: Assertion Strength (RQ1), Edge-Case Coverage (RQ2), and Flakiness Potential (RQ3). Our results present a nuanced inversion of traditional assumptions. AI agents performed better than humans in Edge-Case Coverage, with almost twice the variety of boundary checks (Variety Score: 0.62 vs 0.32) and a higher frequency of null-safety testing (13.40% vs. 8.3%), even though human developers had a slight advantage in Assertion Strength (88.1% strong assertions vs. 85.37% for agents). But this thoroughness comes at a price: due mostly to their reliance on file I/O and non-deterministic logic, agent-generated tests exhibited a higher risk of flakiness (Candidate Rate: 0.41 vs. 0.30). These findings suggest that while AI agents excel at rigorous boundary testing, they lack the “environmental awareness” needed to write stable, hermetic tests.
Preet Jhanglani, Zeel Desai, Vidhi Kansara et al.· Annual International Compute...· 0 citations