Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2024

Abstract

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

Keywords

Foundation models, correctness, creativity

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

FORGE '24: Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering, Lisbon, Portugal, April 14

First Page

119

Last Page

123

ISBN

9798400706097

Identifier

10.1145/3650105.3652302

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1145/3650105.3652302

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