5 papers
A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoption
Yegor Denisov-Blanch, Shyam Agarwal, Pavel Azaletskiy +5
Coding agents increase development velocity but also technical debt. Prior work reports only average effects across adopters, hiding wide differences between teams. We introduce RA…
3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse
Shyam Agarwal, Courtney Miller, Christian Kästner +1
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 st…
AI Writes Faster Than Humans Can Review: A Longitudinal Study of an Enterprise 2x Mandate
Hao He, Shyam Agarwal, Yegor Denisov-Blanch +3
Enterprises increasingly mandate AI coding tools and report large productivity gains, yet longitudinal evidence on how such a mandate unfolds is scarce. In this paper, we present a…
AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development
Shyam Agarwal, Hao He, Bogdan Vasilescu
Large language model (LLM) based coding agents increasingly act as autonomous contributors that generate and merge pull requests, yet their real-world effects on software projects…
Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects
Hao He, Courtney Miller, Shyam Agarwal +2
Large language models (LLMs) have demonstrated the promise to revolutionize the field of software engineering. Among other things, LLM agents are rapidly gaining momentum in softwa…