Abstract
Software testing is intended to find bugs/faults that can occur in the software components currently under development. Software fault prediction (SFP) helps in achieving this goal by predicting the probability of fault occurrence in the software modules before the testing phase.
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References
Amasaki, S.: Cross-version defect prediction using cross-project defect prediction approaches: does it work? In: Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 32–41 (2018)
Boetticher, G.: The PROMISE repository of empirical software engineering data. http://promisedata.org/repository (2007)
D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: Proceedings of 7th IEEE Working Conference on Mining Software Repositories (MSR), pp. 31–41 (2010)
Elish, M.O., Aljamaan, H., Ahmad, I.: Three empirical studies on predicting software maintainability using ensemble methods. Soft Comput. 19(9), 1–14 (2015)
Elish, M.O., Al-Yafei, A.H., Al-Mulhem, M.: Empirical comparison of three metrics suites for fault prediction in packages of object-oriented systems: a case study of eclipse. Adv. Eng. Softw. 42(10), 852–859 (2011)
Holmes, G., Donkin, A., Witten, I.H.: Weka: A machine learning workbench. In: Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361 (1994)
Jureczko, M., Madeyski, L.: Towards identifying software project clusters with regard to defect prediction. In: Proceedings of the 6th International Conference on Predictive Models in Software Engineering, p. 9 (2010)
Kumar, S., Rathore, S.S: Software Fault Prediction a Road Map. Springer Brief in Computer Science, 1st edn, pp. 1–72. Springer (2018)
Mendes-Moreira, J., Soares, C., Jorge, A.M., Sousa, J.F.D.: Ensemble approaches for regression: A survey. ACM Comput. Surv. (CSUR) 45(1), 10 (2012)
Rathore, S.S., Kumar, S.: An empirical study of some software fault prediction techniques for the number of faults prediction. Soft. Comput. 21(24), 7417–7434 (2017)
Rathore, S.S., Kumar, S.: Ensemble methods for the prediction of number of faults: a study on eclipse project. In 11th International Conference on Industrial and Information Systems (ICIIS), pp. 540–545 (2016)
Shatnawi, R., Li, W.: The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process. J. Syst. Softw. 81(11), 1868–1882 (2008)
Zhu, X., He, Y., Cheng, L., Jia, X., Zhu L.: Software change‐proneness prediction through combination of bagging and resampling methods. J. Softw.: Evol. Process., e2111 (2011)
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Rathore, S.S., Kumar, S. (2019). Homogeneous Ensemble Methods for the Prediction of Number of Faults. In: Fault Prediction Modeling for the Prediction of Number of Software Faults. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-7131-8_3
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DOI: https://doi.org/10.1007/978-981-13-7131-8_3
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