篇名 |
Mutation Operator Reduction for Cost-effective Deep Learning Software Testing via Decision Boundary Change Measurement
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並列篇名 | Mutation Operator Reduction for Cost-effective Deep Learning Software Testing via Decision Boundary Change Measurement |
作者 | Li-Chao Feng、Xing-Ya Wang、Shi-Yu Zhang、Rui-Zhi Gao、Zhi-Hong Zhao |
英文摘要 | Mutation testing has been deemed an effective way to ensure Deep Learning (DL) software quality. Due to the requirements of generating and executing mass mutants, mutation testing suffers low-efficiency problems. In regard to traditional software, mutation operators that are hard to cause program logic changes can be reduced. Thus, the number of the mutants, as well as their executions, can be effectively decreased. However, DL software relies on model logic to make a decision. Decision boundaries characterize its logic. In this paper, we propose a DL software mutation operator reduction technique. Specifically, for each group of DL operators, we propose and use DocEntropy to measure the model’s decision boundary changes among mutants generated and the original model. Then, we select the operator group with the highest entropy value and use the involved operators for further mutation testing. An empirical study on two DL models verified that the proposed approach could lead to cost-effective DL software mutation testing (i.e., 33.61% mutants and their executions decreased on average) and archive more accuracy mutation scores (i.e., 9.45% accuracy increased on average).
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起訖頁 | 601-610 |
關鍵詞 | DL software、Mutation testing、Decision boundary、Mutation operator reduction |
刊名 | 網際網路技術學刊 |
期數 | 202205 (23:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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