Shrinking Counterexamples in Property-Based Testing with Genetic Algorithms

Fang Yi Lo, Chao Hong Chen, Ying-Ping Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, genetic algorithms are proposed to shrink counterexamples found by QuickChick, a property-based testing framework for Coq. In order to make the outcome of property-based testing humanly understandable and inspectable, genetic algorithms are brought into the realm of rigorous software development as shrinkers capable of handling a broad range of data structures. In the present study, two showcases, merge sort and insertion of red-black trees, are investigated for illustrative purposes. Due to the lack of relevant results existing in the literature, two baseline methods, random sample and random walk are included in the experiments for comparison with the proposed genetic algorithm. The obtained results indicate that the proposal is effective since the program mistake can be identified with ease by examining the shrunk counterexamples and also that the adopted genetic algorithm statistically significantly outperforms random sample and random walk in both counterexample sizes and running time.

原文American English
主出版物標題2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728169293
DOIs
出版狀態Published - 7月 2020
事件2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom
持續時間: 19 7月 202024 7月 2020

出版系列

名字2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference2020 IEEE Congress on Evolutionary Computation, CEC 2020
國家/地區United Kingdom
城市Virtual, Glasgow
期間19/07/2024/07/20

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