The study investigates how autonomous coding agents interact with technical documentation, analyzing 557 coding sessions and 33,097 pull requests. Findings reveal that agents primarily engage with agent-facing artefacts, show weak links between documentation consultation and code editing, lack explicit validation sequences, and tend to consult documentation after code changes. The authors propose a two‑lobed cycle model of agent‑documentation interaction and challenge assumptions about actionability and verifiability of agent‑friendly documentation.
arXiv:2608. 20195v1 Announce Type: cross Abstract: Technical documentation is written for human developers, but an increasing share of software changes is now authored by autonomous coding agents.
arXiv:2609.39909v1 Announce Type: cross Abstract: We introduce DoGBENCH (Documentation Generation Benchmark), to our knowledge, the first benchmark for generating and maintaining real user-facing sof...
arXiv:2608.29204v1 Announce Type: cross Abstract: Generative AI-based software engineering agents are becoming routine contributors to real-world software projects. On GitHub, developers can assign t...
arXiv:2608. 10037v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external tools to accomplish complex real-world tasks, making tool documentation a critical grounding resource for LLM agents.
arXiv:2509.21891v3 Announce Type: replace-cross Abstract: Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been...
Spec-Driven Development (SDD) is a practice where developers and AI coding agents write specifications to guide software implementation, aiming to preserve the intent behind natural‑language instructions. This study analyzes SDD through Reddit discussions, interviews with 21 practitioners (including eight tool makers), and a large corpus of 470,795 spec files from 73,030 GitHub repositories. Findings reveal that while specs often include implementation details that aid agents, they also risk decay; tool-specific structures influence perception, and benefits vary between individuals and teams, with SDD mainly used for new features and involving higher planning and review costs.
The paper explores whether natural‑language documentation aids coding agents in fixing software bugs and introduces a roundtrip benchmark that evaluates code descriptions by regenerating code and testing it. It finds that description completeness, not length, determines fidelity, and presents an optimizer that can produce fully faithful descriptions that generalize to new files. However, experiments across two model families and ten repositories show that such compact documentation does not improve an agent’s ability to resolve real repository issues compared to using the issue alone.
arXiv:2606. 09090v1 Announce Type: cross Abstract: Developers increasingly provide AI coding assistants with persistent context through configuration files such as CLAUDE.
arXiv:2606. 13468v1 Announce Type: cross Abstract: AI coding agents are increasingly used to generate pull requests (PRs) that propose code fixes in software projects.
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. 08500v1 Announce Type: cross Abstract: Software engineering agents (SWE agents) increasingly work through tool-mediated trajectories in real repositories, yet their behavior remains difficult to characterize in concrete, observable terms.
arXiv:2606. 13449v1 Announce Type: cross Abstract: AI-agents (e.