In OSS repos that commit AI chat logs, AI use is heavier in smaller, less collaborative projects; chats almost always precede commits, quality signals do not broadly worsen, and developers trust their own AI code more than others'.
Medappa, Murat M
7 Pith papers cite this work. Polarity classification is still indexing.
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Longitudinal panel study of 802 developers shows an enterprise AI coding mandate doubled per-capita merged pull requests to 2.09x baseline, with gains associated with AI adoption and accumulated use while review processes automated.
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
Comparative case study on a postgraduate Java assignment finds PureAI and PostAI projects simpler with lower code smell density than PreAI but show oversimplification and weaker responsibility separation.
Meta-analysis of 23 studies shows moderate productivity gains from GenAI coding assistants (Hedges' g=0.33) but no significant effect on learning (g=0.14).
Agentic AI systems are shifting software engineering from line-level code generation to delegated repository-scale execution under supervision, with SWE-bench performance rising from 1.96% to 78.4% and productivity gains of 13.6-55.8%.
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.
citing papers explorer
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From Conversation to Contribution: Characterizing Coding Agent in Open-Source Software
In OSS repos that commit AI chat logs, AI use is heavier in smaller, less collaborative projects; chats almost always precede commits, quality signals do not broadly worsen, and developers trust their own AI code more than others'.
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AI Writes Faster Than Humans Can Review: A Longitudinal Study of an Enterprise 2x Mandate
Longitudinal panel study of 802 developers shows an enterprise AI coding mandate doubled per-capita merged pull requests to 2.09x baseline, with gains associated with AI adoption and accumulated use while review processes automated.
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The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
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Can LLMs Produce Better Object-Oriented Designs than Human-Involved Development?
Comparative case study on a postgraduate Java assignment finds PureAI and PostAI projects simpler with lower code smell density than PreAI but show oversimplification and weaker responsibility separation.
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A meta-analysis of the effect of generative AI on productivity and learning in programming
Meta-analysis of 23 studies shows moderate productivity gains from GenAI coding assistants (Hedges' g=0.33) but no significant effect on learning (g=0.14).
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Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering
Agentic AI systems are shifting software engineering from line-level code generation to delegated repository-scale execution under supervision, with SWE-bench performance rising from 1.96% to 78.4% and productivity gains of 13.6-55.8%.
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Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.