Home > Archives > IJSRST184517
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%.
Ajay Kumar Shrivastava, Dr. Ekbal Rashid
Stacked Regression, Bug Prediction, Cost Estimation, Rayleigh defect density, Software Project Bugs
- X. Huo, M. Li, and Z.-H. Zhou, "Learning unified features from natural and programming languages for locating buggy source code," in Proceedings of IJCAI'2016
- S. Wang, T. Liu, and L. Tan, "Automatically learning semantic features for defect prediction," in ICSE'16: Proc. of the International Conference on Software Engineering, 2016
- V. Raychev, M. Vechev, and E. Yahav, "Code completion with statistical language models," in ACM SIGPLAN Notices, vol. 49, no. 6. ACM, 2014, pp. 419-428.
- Jian Li, Pinjia He, Jieming Zhu, and Michael R. Lyu.2017. "Software Defect Prediction via Convolutional Neural Network" at IEEE International Conference on Software Quality, Reliability and Security, 2017
- Z. He, F. Peters, T. Menzies, and Y. Yang, "Learning from open-source projects: An empirical study on defect prediction," in ESEM'13: Proc. of the International Symposium on Empirical Software Engineering and Measurement, 2013.
- N. A. Abou-Elheggag, Estimation for Rayleigh distribution using progressive first-failure censored data, Journal of Statistics Applications and Probability 2(2) (2013) 171-182.
- J. Wang, B. Shen, and Y. Chen, "Compressed c4. 5 models for software defect prediction," in QSIC'12: Proc. of the International Conference on Quality Software, 2012.
- T. Gyimothy, R. Ferenc, I. Siket, "Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction", IEEE Transactions on Software Engineering, vol. 31, no.10, pp. 897-910, 2005.
- S.W. Haider, J.W. Cangussu, K.M.L. Cooper, R. Dantu, "Estimation of Defects Based on Defect Decay Model: ED3M", IEEE Transactions on Software Engineering, vol. 34, no. 3, pp. 336-356, 2008.
- R. M. El-Sagheer, Inferences using type-II progressively censored data with binomial removals, Arabian Journal of Mathematics 4 (1) (2015) 127-139.
- K. Herzig, S. Just, and A. Zeller. It's not a bug, it's a feature: how misclassification impacts bug prediction. In ICSE'13, pages 392-401.
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