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Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction
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DOI：10.1109/ACCESS.2021.3052149.png)
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摘要
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Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.
Keyword:
Software
Optimization
Software algorithms
Feature extraction
Support vector machines
Prediction algorithms
Genetic algorithms
Software fault prediction
feature selection
binary whale optimization algorithm
adaptive synthetic sampling
classification
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
A Systematic Literature Review on Fault Prediction Performance in Software Engineering软件工程中故障预测性能的系统文献综述
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APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
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