A SURVEY OF FEATURE SELECTION METHODS FOR SOFTWARE DEFECT PREDICTION MODELS
Feature selection is a technique used to select an optimal feature subset from the original input features according to a specific criterion. The criterion is often formulated as an objective function that finds which features are most appropriate for some tasks at hand. The reason why it is interesting to find a subset of features is because that it always easier to solve a problem in a lower dimension. This helps in understanding the nonlinear mapping between input and output variables. This paper reviewed the basic Feature Selection Techniques for Software Defect Prediction Model and their domain applications. The Subsets selection are categorized into three distinct models and are discussed in a concise form to provide young researchers with the general methods of Subset Selection. Support Vector Machine with Recursive Feature Elimination for both Logistic Regression and Random Forest was introduced to evaluate the performance between filter, wrapper, and embedded feature selection techniques. Hence, the research proposes an Embedded Feature Selection techniques for consistency of a subset of software metrics analysis.
Bins J. and B. A. Draper. (2015). Feature selection from huge feature sets," in Proc. 8th International Conference on Computer Vision (ICCV-01), Vancouver, British Columbia, Canada, IEEE Computer Society: 159â€“165.
Bowes David, Tracy Hall, and Jean Petri. (2017). Software defect prediction: do different Classifiers find the same defects? This article is published with open access at Springerlink.com.
Choudhary Garvit Rajesh, Sandep Kumar, Kuldeep Kumar, and Alok Mishra. (2018). Empirical Analysis of Change Metrics for Software Fault Prediction. Computer and Electrical Engineering, Elsevier.com/locate/compeleceng, 67:15-24.
Ghotra Baljinder, Shane McIntosh, Ahmed E. Hassan. (2017). A Large-Scale Study of the Impact of Feature Selection Techniques on Defect Classification Models.2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR).
Hoque Nazrul, Mihir Singh, Dhruba K. Bhattacharyya. (2018). EFS-MI: an Ensemble Feature Selection Method for Classification. Complex & Intelligent Systems ISSN: 2199-4536.
Jiarpakdee Jirayus, Chakkrit Tantithamthavorn, Christoph Treude (2018). Autospearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Modelsâ€ 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME) 2018.
Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and Ahmed E. Hassan. (2018).The Impact of Correlated Metrics on Defect Models IEEE Transactions on Software Engineering 2018.
JoviÄ‡ A., K. BrkiÄ‡, and N. BogunoviÄ‡. (2015). A review of feature selection methods with Applications. 763354 MIPRO.
Lal Thomas, Olivier Chapelle, Jason Weston, and AndrÃƒlâ€™ Elisseeff. (2006). Embedded Methods in Feature Extraction. Volume 207 of Studies in Fuzziness and Soft Computing, pages 137â€“165. Springer Berlin Heidelberg.
Liu C., D. Jiang, and W. Yang. (2014). Global geometric similarity scheme for feature selection in fault diagnosis. Expert Systems with Applications. 41(8): 3585â€“3595.
Kitchenham, B.A (2007). Guidelines for Performing Systematic Literature Review in Software Engineering; Technical Report. EBSE-2007-001; Keele University and Durham University: Staffordshire, UK.
Okutan Ahmet. (2018). Use of Source Code Similarity Metrics in Software Defect Prediction. arXiv: 1808.10033v1 [cs.SE].
Raukas Hans. (2017). Some Approaches for Software Defect Prediction. Ph.DDissertation UNIVERSITY OF TARTU Institute of Computer Science Computer Science Curriculum.
Tantithamthavorn Chakkrit, Shane Mcintosh, Ahmed E. Hassan, Kenichi M. (2018). The Impact of Automated Parameter Optimization on Defect Prediction Models. IEEE Transaction on Software Engineering.
FUDMA Journal of Sciences