Creative and Correct: Requesting Diverse Code Solutions from AI Foundation Models
CoRR(2024)
摘要
AI foundation models have the capability to produce a wide array of responses
to a single prompt, a feature that is highly beneficial in software engineering
to generate diverse code solutions. However, this advantage introduces a
significant trade-off between diversity and correctness. In software
engineering tasks, diversity is key to exploring design spaces and fostering
creativity, but the practical value of these solutions is heavily dependent on
their correctness. Our study systematically investigates this trade-off using
experiments with HumanEval tasks, exploring various parameter settings and
prompting strategies. We assess the diversity of code solutions using
similarity metrics from the code clone community. The study identifies
combinations of parameters and strategies that strike an optimal balance
between diversity and correctness, situated on the Pareto front of this
trade-off space. These findings offer valuable insights for software engineers
on how to effectively use AI foundation models to generate code solutions that
are diverse and accurate.
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