Homogeneous Ensemble Methods for the Prediction of Number of Faults

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.

[1]  Lei Zhu,et al.  Software change‐proneness prediction through combination of bagging and resampling methods , 2018, J. Softw. Evol. Process..

[2]  Mahmoud O. Elish,et al.  Empirical comparison of three metrics suites for fault prediction in packages of object-oriented systems: A case study of Eclipse , 2011, Adv. Eng. Softw..

[3]  Irfan Ahmad,et al.  Three empirical studies on predicting software maintainability using ensemble methods , 2015, Soft Comput..

[4]  Sandeep Kumar,et al.  Ensemble methods for the prediction of number of faults: A study on eclipse project , 2016, 2016 11th International Conference on Industrial and Information Systems (ICIIS).

[5]  Alípio Mário Jorge,et al.  Ensemble approaches for regression: A survey , 2012, CSUR.

[6]  Sousuke Amasaki,et al.  Cross-Version Defect Prediction using Cross-Project Defect Prediction Approaches: Does it work? , 2018, PROMISE.

[7]  Raed Shatnawi,et al.  The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process , 2008, J. Syst. Softw..

[8]  Thomas J. Ostrand,et al.  \{PROMISE\} Repository of empirical software engineering data , 2007 .

[9]  Michele Lanza,et al.  An extensive comparison of bug prediction approaches , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).

[10]  Sandeep Kumar,et al.  An empirical study of some software fault prediction techniques for the number of faults prediction , 2017, Soft Comput..

[11]  Lech Madeyski,et al.  Towards identifying software project clusters with regard to defect prediction , 2010, PROMISE '10.

[12]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.