This preprint studies how coding agents are changing code review by combining GitHub observations with a large-scale synthesis of practitioner discourse. From 38,709 engineering blogs and Reddit documents, the authors code a stratified random sample of 3,100 using an LLM-assisted pipeline. They construct a causal model containing 26 constructs and 67 relationships, including 64 directed and 3 contested relationships. The central claim is that review is the control point determining whether agents improve or harm software outcomes, with effects moderated by human expertise and review-process design. GitHub patterns appear sensitive to analytical choices.
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