arXiv AI
Jun 12
Toward Instructions-as-Code: Understanding the Impact of Instruction Files on Agentic Pull Requests
arXiv:2606. 13449v1 Announce Type: cross Abstract: AI-agents (e.
Understanding the Rejection of Fixes Generated by Agentic Pull Requests -- Insights from the AIDev Dataset
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.
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arXiv AI
Jul 21
AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.
arXiv AI
Sep 1
AgentLogs: A Dataset for Opening the Black Box of GitHub's Cloud Agent
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 AI
Sep 1
How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
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...
Hugging Face Trending Papers
Aug 20
From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
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 AI
Aug 21
From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical 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 AI
Aug 10
Online Monitoring and Corrective Steering of Programming Agents
arXiv:2608. 06701v1 Announce Type: cross Abstract: Fixing GitHub issues in large-scale projects is a long-horizon task, especially when a fix requires changes across multiple locations or the issue description lacks the information needed to localize and repair it.
arXiv AI
Aug 5
Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
arXiv:2606. 02965v2 Announce Type: replace Abstract: As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion.
arXiv AI
Jun 3
What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents
arXiv:2606. 02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.
arXiv AI
Jul 10
3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse
arXiv:2607. 07980v1 Announce Type: cross Abstract: Coding agents now author entire pull requests, and practitioners sharply disagree about what this does to code review: whether it becomes the bottleneck, whether human review is still necessary, and whether it quietly erodes the understanding that it once built.
arXiv AI
Aug 11
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
arXiv AI
Sep 1
Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study
The paper presents a systematic analysis of five state‑of‑the‑art automated program repair agents, tracing their decision‑making across 500 real‑world repair tasks. It finds that while the agents perform well on simple fixes, they struggle with logic‑intensive bugs, often producing verbose, overfitted patches that pass tests without addressing root causes. Key bottlenecks identified include poor test generation, limited regression test selection, and reliance on primitive tooling without access to debuggers or advanced program analysis tools.