Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/spe.2941 | SOFTWARE-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
fault prediction, genetics algorithm, machine learning, optimization algorithms, Whale algorithm | Journal | 51 |
Issue | ISSN | Citations |
5 | 0038-0644 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiba Alsghaier | 1 | 1 | 0.69 |
| Mohammed Akour | 2 | 5 | 4.81 |