c-CRAB benchmark shows state-of-the-art code review agents solve only around 40% of tasks derived from human reviews, suggesting potential for human-AI collaboration.
On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents
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Analysis of 10K GitHub repositories shows standardization of README.md, .gitignore and LICENSE, dominance of GitHub Actions, shift toward YAML/JSON/TOML, growth of Dockerfiles, and early LLM-related files.
Developers are already embedding guidance on fairness, accessibility, sustainability, tone, and privacy into repository-level files for AI agents, creating a developer-authored governance layer.
Comparative review of AI coding tool ToS shows responsibility for code quality and compliance shifted to users, with policy misalignment for autonomous agents, plus a research roadmap.
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.
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Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap
Comparative review of AI coding tool ToS shows responsibility for code quality and compliance shifted to users, with policy misalignment for autonomous agents, plus a research roadmap.