Author

T. A. Ghaleb

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Review Aug 2026

AI-to-AI Code Reviews of GitHub Pull Requests

AI coding agents are increasingly integrated into software development workflows, operating on both sides of the pull-request (PR) process: AI authoring agents create or modify PRs, while AI reviewers evaluate them. This creates a closed loop in which one AI coding agent reviews a contribution attributed to another. We construct a large-scale dataset of AI-to-AI code review by linking AI-attributed PRs with AI-attributed review events from CodAGE, a public dataset of coding-agent-generated GitHub events. Our dataset contains 248,641 unique AI-attributed PRs that received at least one AI-attributed review. Of these, 45,269 received cross-product review and 208,145 received same-product review; 4,773 PRs received both. Cross-product AI-to-AI review occurred in approximately 1.6% of identified agent-authored PRs but was substantial in absolute terms, and its volume increased by more than two orders of magnitude from 2025-Q1 to 2025-Q3. Reviewer output varied across author-reviewer configurations. CodeRabbit labeled 35.0% of its comments on Claude Code-authored PRs as refactor comments, compared with 10.5% on Copilot-authored PRs, although this difference may reflect characteristics of the PRs rather than the reviewer. For three of four dual-role reviewers, mean comments per PR were 58-65% higher in the same-product group, although effect sizes were small or negligible and the difference was concentrated in the upper tail. Among pairs with complete, nonnegative timestamps, the observed median latency was 1.2 minutes for cross-product pairs and 4.7 minutes for same-product pairs; differential timestamp availability and reviewer composition limit this comparison. Overall, closed-loop AI-to-AI review is increasing but remains a minority of identified agent activity, with review output varying across authoring-agent groups and product configurations.

Niruthiha Selvanayagam, T. A. Ghaleb · 0 citations

Preprint Aug 2026

Doc2CI: A Multi-Service Study of CI Configuration Generation Using Large Language Models

Adopting Continuous Integration (CI) often requires writing YAML configurations that are error-prone and challenging to maintain. Despite increasing LLM use in software engineering, their ability to generate CI configurations from natural language across services and model families remains unclear. This paper presents a large empirical study on using LLMs to generate CI configurations. We introduce DOC2CI, a benchmark of 3,363 description-to-YAML pairs collected from the official documentation of four CI services, and evaluate 14 open-weight models from 7B-34B parameters together with GPT-4o and GPT-4.1, producing over 53,000 configurations. We assess both reference alignment and schema validity to determine whether the generated configurations are structurally valid. We further develop a failure taxonomy from a manual analysis of 385 configurations and examine why LLMs disagree. Across models and services, exact reference reproduction never exceeds 3.1%, and while 97% of outputs parse as YAML, only 71% satisfy service schemas. Larger models improve structural validity, but code specialization provides no consistent advantage over comparable general models. Model differences are driven largely by output completeness: for the same request, some models generate the expected fragment while others produce a full workflow. Finally, a training-free schema-guided repair method improves schema validity to 94%, while fine-tuning improves similarity to documentation but reduces standalone validity. This suggests that similarity and validity are distinct objectives for CI generation and motivate schema-aware evaluation and tooling for LLM-based configuration generation.

T. A. Ghaleb · 0 citations