Archer automates agentic code review for LLVM optimizations and reports finding semantic bugs in 21% of recent open PRs and 11% of closed PRs.
Training large language models to comprehend llvm ir via feedback-driven optimization,
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 3years
2026 3representative citing papers
A survey of 457 SE researchers finds widespread GenAI use concentrated in writing and ideation, with productivity gains but persistent concerns over accuracy, bias, and the need for clearer governance rules.
Interviews with 22 developers produced a preliminary reliance-control framework that uses levels of control over AI to identify appropriate reliance in software engineering.
citing papers explorer
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Archer: Towards Agentic Review for Compiler Optimizations
Archer automates agentic code review for LLVM optimizations and reports finding semantic bugs in 21% of recent open PRs and 11% of closed PRs.
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Taking a Pulse on How Generative AI is Reshaping the Software Engineering Research Landscape
A survey of 457 SE researchers finds widespread GenAI use concentrated in writing and ideation, with productivity gains but persistent concerns over accuracy, bias, and the need for clearer governance rules.
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Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineering
Interviews with 22 developers produced a preliminary reliance-control framework that uses levels of control over AI to identify appropriate reliance in software engineering.