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
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 6verdicts
UNVERDICTED 6roles
background 1polarities
background 1representative citing papers
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.
ASE-26 is a proposed undergraduate curriculum for agentic software engineering organized around an evolutionary spiral of intent and build, with 21 modules and pedagogical commitments for agent-co-produced work.
citing papers explorer
-
ASE-26: a curriculum for agentic software engineering as a discipline
ASE-26 is a proposed undergraduate curriculum for agentic software engineering organized around an evolutionary spiral of intent and build, with 21 modules and pedagogical commitments for agent-co-produced work.