A Meta-Stacked Software Bug Prognosticator Classifier
Keywords:
Stacked Regression, Bug Prediction, Cost Estimation, Rayleigh defect density, Software Project BugsAbstract
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%.
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