arXiv AI
How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 2
SWE-chat: Coding Agent Interactions From Real Users in the Wild
arXiv:2604.20779v2 Announce Type: replace Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful...
arXiv AI
Sep 30
Is Agent Code Less Maintainable Than Human Code?
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 Machine Learning
Jul 27
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
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 AI
Oct 9
Runnable Commit Untangling for Coding Agents
The paper "Runnable Commit Untangling for Coding Agents" introduces RucTangle, an agentic method that untangles large, tangled patches from coding agents while ensuring each commit keeps the code runnable. It also presents TangleEval, an evaluation framework that measures how untangled commit histories aid agents in repairing bugs. Experiments on 131 agent-generated patches show RucTangle produces fully runnable histories, outperforming baselines that leave 20.6%-37.4% of commits unrunnable, and improves bug‑repair success by 5.2% absolute in pass@1.
arXiv AI
Jul 22
Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality
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 AI
Oct 7
Catching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale
The paper introduces FlowAgent, an AI agent deployed at Google to automatically repair test failures in the pre-submit continuous integration workflow. FlowAgent uses a ReAct-style generate-and-validate loop with strict latency and quality filters, and was evaluated on 195 real-world failures with a 67.18% accuracy rate. After deployment, it suggested fixes on 295,508 changes, with developers previewing 65,069 and applying 28,554, and received positive feedback from interviews.