Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning
Open accessAbstract
Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.
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- Authors
- Mafarja, M., Thaher, T., Al-Betar, M. A., Too, J., Awadallah, M. A., Abu Doush, I., & Turabieh, H.
- Affiliations
- Birzeit University (PS)
- Al-Quds University (PS)
- Universiti Teknikal Malaysia Melaka (MY)
- Ajman University (AE)
- University of Missouri (US)
- Corresponding author
- Mafarja, Majdi
- Thaher, Thaer
- Too, Jingwei
- Awadallah, Mohammed A.
- Turabieh, Hamza
- ORCID
- Venue
- Applied Intelligence
- Published
- 2023-02-09
- Volume
- 53
- Issue
- 15
- Pages
- 18715-18757
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- PMC
- PMC9909674
- OpenAlex
- W4319659826
- Cited by
- 91
- References
- 89
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 42 · 2023: 12
- FWCI
- 21.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Applied Intelligence (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Reliability and Analysis Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- software defect prediction, feature selection, whale optimization algorithm, grey wolf optimizer, Harris Hawks Optimization, hybrid metaheuristic, random forest, dimensionality reduction, PROMISE dataset, classifier comparison, data resampling
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