Abstract
The quality of software is enormously affected by the faults associated with it. Detection of faults at a proper stage in software development is a challenging task and plays a vital role in the quality of the software. Machine learning is, now a days, a commonly used technique for fault detection and prediction. However, the effectiveness of the fault detection mechanism is impacted by the number of attributes in the publicly available datasets. Feature selection is the process of selecting a subset of all the features that are most influential to the classification and it is a challenging task. This paper thoroughly investigates the effect of various feature selection techniques on software fault classification by using NASA’s some benchmark publicly available datasets. Various metrics are used to analyze the performance of the feature selection techniques. The experiment discovers that the most important and relevant features can be selected by the adopted feature selection techniques without sacrificing the performance of fault detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, S., Tomar, D.: A feature selection based model for software defect prediction. Int. J. Adv. Sci. Technol. 65, 39–58 (2014)
Anbu, M., Anandha Mala, G.S.: Feature selection using firefly algorithm in software defect prediction. Cluster Comput., 1–10 (2017)
Arasteh, B.: Software fault-prediction using combination of neural network and Naive Bayes algorithm. J. Netw. Technol. 9(3), 94 (2018)
Chen, X., Shen, Y., Cui, Z., Ju, X.: Applying feature selection to software defect prediction using multi-objective optimization. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), pp. 54–59. IEEE, July 2017
Crack, T.F.: A note on Karl Pearson’s 1900 Chi-squared test: two derivations of the asymptotic distribution, and uses in goodness of fit and contingency tests of independence, and a comparison with the exact sample variance chi-square result. SSRN Electron. J. (2018)
Akalya Devi, C., Surendiran, B., Kannammal, K.E.: A study of feature selection methods for software fault prediction model. In: Proceedings of the International Conference on Network, Intelligence and Computing Technologies (ICNICT 2011), Tamil Nadu, India, pp. 1–5 (2011)
Fawagreh, K., Gaber, M.M., Elyan, E.: Random forests: from early developments to recent advancements. Syst. Sci. Control Eng. 2(1), 602–609 (2014)
Felix, E.A., Lee, S.P.: Integrated approach to software defect prediction. IEEE Access 5, 21524–21547 (2017)
Gray, D., Bowes, D., Davey, N., Sun, Y., Christianson, B.: The misuse of the NASA metrics data program data sets for automated software defect prediction. In: 15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011), pp. 96–103. IET (2011)
Ibrahim, D.R., Ghnemat, R., Hudaib, A.: Software defect prediction using feature selection and random forest algorithm. In: 2017 International Conference on New Trends in Computing Sciences (ICTCS), pp. 252–257. IEEE, October 2017
Jakhar, A.K., Rajnish, K.: Software fault prediction with data mining techniques by using feature selection based models. Int. J. Electr. Eng. Inf. 10(3), 447–465 (2018)
Jia, L.: A hybrid feature selection method for software defect prediction. IOP Conf. Ser. Mater. Sci. Eng. 394(3), 032035 (2018)
Jiang, Y., Li, M., Zhou, Z.-H.: Software defect detection with ROCUS. J. Comput. Sci. Technol. 26(2), 328–342 (2011)
Kakkar, M., Jain, S.: Feature selection in software defect prediction: a comparative study. In 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), pp. 658–663. IEEE, January 2016
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256 (1992)
McHugh, M.L.: The Chi-square test of independence. Biochemia Medica, 143–149 (2013)
Mishra, M., Srivastava, M.: A view of artificial neural network. In: 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), pp. 1–3. IEEE, August 2014
Nugroho, A., Chaudron, M.R.V., Arisholm, E.: Assessing UML design metrics for predicting fault-prone classes in a Java system. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 21–30. IEEE, May 2010
Joanne Peng, C.-Y., Lee, K.L., Ingersoll, G.M.: An introduction to logistic regression analysis and reporting. J. Educ. Res. 96(1), 3–14 (2002)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1–2), 1–39 (2010)
Shepperd, M., Song, Q., Sun, Z., Mair, C.: Data quality: some comments on the NASA software defect data sets. 2010(9), 1–13 (2013)
Singhal, R., Rana, R.: Chi-square test and its application in hypothesis testing. J. Pract. Cardiovasc. Sci. 1(1), 69 (2015)
Son, L.H., et al.: Empirical study of software defect prediction: a systematic mapping. Symmetry 11(2) (2019)
Song, Q., Jia, Z., Shepperd, M., Ying, S., Liu, J.: A general software defect-proneness prediction framework. IEEE Trans. Software Eng. 37(3), 356–370 (2011)
Wahono, R.S., Herman, N.S.: Genetic feature selection for software defect prediction. Adv. Sci. Lett. 20(1), 239–244 (2014)
Webb, G.I., Keogh, E., Miikkulainen, R., Sebag, M.: Naïve Bayes. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 713–714. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_576
Xu, Z., Xuan, J., Liu, J., Cui, X.: MICHAC: defect prediction via feature selection based on maximal information coefficient with hierarchical agglomerative clustering. In: 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), pp. 370–381. IEEE, March 2016
Yousef, A.H.: Extracting software static defect models using data mining. Ain Shams Eng. J. 6(1), 133–144 (2015)
Qiao, Y., Jiang, S., Wang, R., Wang, H.: A feature selection approach based on a similarity measure for software defect prediction. Front. Inf. Technol. Electron. Eng. 18(11), 1744–1753 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tasnim Cynthia, S., Rasul, M.G., Ripon, S. (2019). Effect of Feature Selection in Software Fault Detection. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-33709-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33708-7
Online ISBN: 978-3-030-33709-4
eBook Packages: Computer ScienceComputer Science (R0)