arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
arXiv:2607. 14037v1 Announce Type: cross Abstract: Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development.
Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level.
arXiv:2606. 13298v1 Announce Type: cross Abstract: AI coding tools are now used by a majority of developers, and agentic use of these tools has popularized the practice colloquially called "vibe coding".
arXiv:2607. 21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows.
arXiv:2608. 13884v1 Announce Type: cross Abstract: The rapid adoption of AI coding assistants and autonomous agentic development systems has coincided with major changes in the pace and structure of open-source software engineering.
The study surveys popular, active GitHub repositories across thirteen languages, treating each repository’s AGENTS.md and CLAUDE.md files as instruction sets for coding agents. It finds that most repositories include such instructions, with CLAUDE.md often referencing AGENTS.md, and that these files contain a mix of project knowledge and workflow rules, including varying stances on AI attribution. The analysis reveals that more popular projects tend to have stricter pull‑request rules and AI contribution guidelines, and that LLM and agent projects are over‑represented among adopters.
SWE-Prometheus is a new benchmark that evaluates large language model coding agents on the broader task of improving repository engineering governance, rather than just fixing individual issues. It presents fixed snapshots with open-ended objectives, requiring agents to identify risks, prioritize interventions, and verify changes across six governance dimensions. The benchmark includes 60 repositories and reports metrics such as Normalized Governance Improvement and behavior‑breakage rates, providing a nuanced view of how different models affect governance artifacts and execution‑backed improvements.
arXiv:2607. 10856v1 Announce Type: cross Abstract: The rise of Software Engineering (SE) agents, i.
arXiv:2605.29442v2 Announce Type: replace-cross Abstract: AI coding agents increasingly act directly within software environments, yet existing analyses of their failures rely on benchmark trajectori...
arXiv:2606.21804v2 Announce Type: replace-cross Abstract: Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding age...
arXiv:2607. 03691v1 Announce Type: cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.