CausalSE applies SCMs and propensity score matching to reveal that causal analysis of prompt engineering on GPT-3 code generation often finds no significant effect where associational analysis suggests improvement.
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cs.SE 2years
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Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
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Rethinking Software Empirical Studies with Structural Causal Models
CausalSE applies SCMs and propensity score matching to reveal that causal analysis of prompt engineering on GPT-3 code generation often finds no significant effect where associational analysis suggests improvement.
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Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code
Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.