FSBOA: feature selection using bat optimization algorithm for software fault detection

被引：0
作者：
Pethe Y.S. [1 ]
Gourisaria M.K. [1 ]
Singh P.K. [2 ]
Das H. [1 ]
机构：
[1] School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar
[2] Department of CSE, Central University of Jammu, J&K, Samba
来源：
Discover Internet of Things | 2024年 / 4卷 / 01期
关键词：
Bat optimization algorithm; Classification algorithm; Feature selection; Software fault prediction;
D O I：
10.1007/s43926-024-00059-4
中图分类号：
学科分类号：
摘要：
Feature selection (FS) plays a crucial role in software fault prediction (SFP), aiming to identify a subset of relevant and discriminative features from a large pool of software metrics. It serves as a critical preprocessing step in building accurate fault prediction models, enabling the identification of potential software faults early in the development life cycle and facilitating effective resource allocation for testing and maintenance activities. The study's objective is to determine how well the bat optimization algorithm (BOA) can extract the features that are most important for correctly predicting software flaws, improve the accuracy of fault prediction, reduce the dimensionality of the feature space, and mitigate the risk of overfitting, thereby enabling more effective resource utilization and better allocation of testing efforts. The forecasting models underwent testing and training utilizing a collection of software metrics, with the datasets undergoing evaluation using several different FS algorithms. An assessment was conducted by contrasting the effectiveness of multiple optimization algorithms, including evolutionary methods such as FS employing genetic algorithm (FSGA), FS employing differential evolution (FSDE), and swarm-based techniques such as FS employing ant colony optimization (FSACO), FS employing particle swarm optimization (FSPSO), FS employing firefly algorithm (FSFA), and FS employing binary grey wolf optimization algorithm (FSBGWO) in relation to FS employing bat optimization algorithm (FSBAO). The results obtained from FSBAO approach demonstrate the effectiveness in solving FS optimization problems with at most accuracy of 98.92%. Furthermore, the experimental results have been statistically validated for the greater efficiency of the proposed FSBAO algorithm. This study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient. © The Author(s) 2024.
引用
收藏
相关论文
共 39 条
[1]  
Catal C., Diri B., Investigating the effect of data set size, metrics sets, and feature selection techniques on software fault prediction problem, Inf Sci, 179, 8, (2009)
[2]  
Liu W., Liu S., Gu Q., Chen X., Chen D., FECS: A cluster based feature selection method for software fault prediction with noises, 2015 IEEE 39Th Annual Computer Software and Applications Conference, pp. 276-281, (2015)
[3]  
Tran H.D., Hanh L.T.M., Binh N.T., Combining feature selection, feature learning and ensemble learning for software fault prediction, 2019 11Th International Conference on Knowledge and Systems Engineering (KSE), pp. 1-8, (2019)
[4]  
Yu L., Liu H., Feature selection for high-dimensional data: A fast correlation-based filter solution, Proceedings of the 20Th International Conference on Machine Learning (ICML-03), Washington, DC, USA, 21–24 August, 2, pp. 856-863, (2003)
[5]  
van Hulse J., Khoshgoftaar T.M., Napolitano A., Experimental perspectives on learning from imbalanced data, Proceedings of the 24Th International Conference on Machine Learning, New York, NY, USA, 20–24 June, 227, pp. 935-942, (2007)
[6]  
Kohavi R., John G.H., Wrappers for feature subset selection, Artif Intell, 97, pp. 273-324, (1997)
[7]  
Huang S.H., Supervised feature selection: a tutorial, Artif Intell Res, 4, pp. 22-37, (2015)
[8]  
Guyon I., Elisseeff A., An introduction to variable and feature selection, J Mach Learn Res, 3, pp. 1157-1182, (2003)
[9]  
Ghojogh B., Samad M.N., Mashhadi S.A., Kapoor T., Ali W., Karray F., Crowley M., Feature selection and feature extraction in pattern analysis: A literature review.
[10]  
Sheikhpour R., Sarram M.A., Gharaghani S., Chahooki M.A.Z., A Survey on semi-supervised feature selection methods, Pattern Recogn, 64, pp. 141-158, (2017)
1234