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Software fault prediction using Whale algorithm with genetics algorithm
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-12-15 , DOI: 10.1002/spe.2941
Hiba Alsghaier 1 , Mohammed Akour 1
Affiliation  

Software fault prediction became an essential research area in the last few years, there are many prediction and optimization techniques that have been developed for fault prediction. In this paper, an approach is developed by integrating genetics algorithm with support vector machine (SVM) classifier and Whale optimization algorithm for software fault prediction. The developed approach is applied to 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset, and Java open source projects are considered as a small‐scale dataset. Results indicate that integrating Genetics algorithm with SVM and Whale algorithm improves the performance of the software fault prediction process when it is applied to large‐scale and small‐scale datasets and overcome the limitations that appeared in the previous studies.

中文翻译:

鲸鱼算法与遗传算法相结合的软件故障预测

在过去的几年中,软件故障预测已成为必不可少的研究领域,已经为故障预测开发了许多预测和优化技术。本文提出了一种将遗传算法与支持向量机分类器和鲸鱼优化算法相结合的方法,用于软件故障预测。所开发的方法适用于24个数据集(12-NASA MDP和12-Java开放源项目),其中NASA MDP被视为大型数据集,而Java开放源项目被视为小型数据集。
更新日期:2020-12-15
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