A Meta-Stacked Software Bug Prognosticator Classifier

Authors(2) :-Ajay Kumar Shrivastava, Dr. Ekbal Rashid

Predicting defects defines the proactive process of classifying the defects that can be found in entire software’s content, within and cross-project codes for producing high quality product with optimized cost. Error prediction in open source software is more crucial due to its inherent complexity and the large repository of contributors. In this paper we present the meta-stacked regression model (MSRM) which improvises the Rayleigh Probabilistic distribution for feature selection estimates. Firstly, a heuristic bug mining approach is adopted to mine the parameters reported by developers and contributors of various Open source projects (Bugzilla, Eclipse, Mozilla) activity logs. In the second part, Stacked Regression is compared to Neural Networks and Linear Support Vector Machine models in terms of the bug prediction performance with Feature importance and Correlation amongst parameters. The results show that the ensemble based Stacked regression has better precision and F-measure compared to simple machine learning models. The MSRM model accurately predicts and classifies bugs with accuracy of 96.8% and reduces the impact of false positives by recall of 71.2%.

Authors and Affiliations

Ajay Kumar Shrivastava
M.Tech Research Scholar, Jharkhand Rai University, Ranchi, Jharkhand, India
Dr. Ekbal Rashid
Department of CSE, Aurora’s Technological and Research Institute, Uppal, Hyderabad, Telangana, India

Stacked Regression, Bug Prediction, Cost Estimation, Rayleigh defect density, Software Project Bugs

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Publication Details

Published in : Volume 4 | Issue 5 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 111-117
Manuscript Number : IJSRST184517
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Ajay Kumar Shrivastava, Dr. Ekbal Rashid, " A Meta-Stacked Software Bug Prognosticator Classifier", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 5, pp.111-117, March-April-2018.
Journal URL : http://ijsrst.com/IJSRST184517

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