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Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project

Wasiur Rhmann, Gufran Ahmad Ansari
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 16
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781799806066|DOI: 10.4018/IJOSSP.2020040103
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MLA

Rhmann, Wasiur, and Gufran Ahmad Ansari. "Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project." IJOSSP vol.11, no.2 2020: pp.33-48. http://doi.org/10.4018/IJOSSP.2020040103

APA

Rhmann, W. & Ansari, G. A. (2020). Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project. International Journal of Open Source Software and Processes (IJOSSP), 11(2), 33-48. http://doi.org/10.4018/IJOSSP.2020040103

Chicago

Rhmann, Wasiur, and Gufran Ahmad Ansari. "Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project," International Journal of Open Source Software and Processes (IJOSSP) 11, no.2: 33-48. http://doi.org/10.4018/IJOSSP.2020040103

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Abstract

Software engineering repositories have been attracted by researchers to mine useful information about the different quality attributes of the software. These repositories have been helpful to software professionals to efficiently allocate various resources in the life cycle of software development. Software fault prediction is a quality assurance activity. In fault prediction, software faults are predicted before actual software testing. As exhaustive software testing is impossible, the use of software fault prediction models can help the proper allocation of testing resources. Various machine learning techniques have been applied to create software fault prediction models. In this study, ensemble models are used for software fault prediction. Change metrics-based data are collected for an open-source android project from GIT repository and code-based metrics data are obtained from PROMISE data repository and datasets kc1, kc2, cm1, and pc1 are used for experimental purpose. Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms. Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.

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