The paper introduces a dataset of the complete development history of a 21,000-line Python tool built entirely by Claude AI, accompanied by two code‑provenance tracing tools and three taxonomies for instruction intent, commit provenance, and response reliability. Analysis reveals that user CLI instructions differ from IDE‑chat instructions, focusing more on comprehension, planning, and consultation; code development is largely proactive; 14.3% of AI code‑generation events contain errors later caught by the AI‑authored test suite; and roughly one in four to five of the AI’s interactive responses contain factual errors.
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...
By Ningzhi Tang, Chaoran Chen, Gelei Xu, Yiyu Shi, Yu Huang, Collin McMillan, Tao Dong, Toby Jia-Jun Li
arXiv:2606. 12329v1 Announce Type: new Abstract: AI coding assistants now support a growing share of software work, from quick scripts to production applications.
By Ripon Chandra Malo, Tong Qiu
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...
By Joachim Baumann, Vishakh Padmakumar, Xiang Li, John Yang, Diyi Yang, Sanmi Koyejo
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.
By Iren Mazloomzadeh, Mohammad Mehdi Morovati, Foutse Khomh
arXiv:2609.12708v2 Announce Type: replace-cross
Abstract: AI coding assistants are becoming co-authors of production software, yet their evaluation centers on functional correctness, leaving open whe...
By Cristina Improta, Pietro Liguori, Domenico Cotroneo