CoT prompting in LLM4Code shows mixed robustness that depends on model family, task structure, and perturbations destabilizing structural anchors, leading to trajectory deformations like lengthening, branching, and simplification.
Large language model-based agents for software engineering: A sur- vey
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AI repair agents solve bugs more reliably when reports include executable reproduction scripts, file-level localization cues, and clear structure, while longer prose reports and human-oriented steps to reproduce show no benefit or hurt.
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'.
citing papers explorer
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Structural Anchors and Reasoning Fragility:Understanding CoT Robustness in LLM4Code
CoT prompting in LLM4Code shows mixed robustness that depends on model family, task structure, and perturbations destabilizing structural anchors, leading to trajectory deformations like lengthening, branching, and simplification.
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What Makes a Good Bug Report for an AI Agent?
AI repair agents solve bugs more reliably when reports include executable reproduction scripts, file-level localization cues, and clear structure, while longer prose reports and human-oriented steps to reproduce show no benefit or hurt.
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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'.