Back to Search
Start Over
Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction.
- Source :
- Intelligent Automation & Soft Computing; 2021, Vol. 29 Issue 3, p669-684, 16p
- Publication Year :
- 2021
-
Abstract
- Software Defect Prediction (SDP) is a dynamic research field in the software industry. A quality software product results in customer satisfaction. However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure. SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user. In this paper, we propose a hybrid approach to address this particular issue. Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) algorithm with benchmark machine-learning classifiers for the better detection of bugs in software modules. Our proposed methodology was tested and validated using 5 open-source National Aeronautics and Space Administration (NASA) data sets from the PROMISE repository: CM1, KC2, JM1, KC1 and PC1. Results were reported in terms of accuracy level and of an AUC with highest accuracy, namely, 94.82%. The results of our experiments indicate that the detection capability of benchmark classifiers can be improved by incorporating Opt-aiNet as a feature selection (FS) method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10798587
- Volume :
- 29
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Intelligent Automation & Soft Computing
- Publication Type :
- Academic Journal
- Accession number :
- 151768449
- Full Text :
- https://doi.org/10.32604/iasc.2021.018405