Reasons for Non-Applicability of Software Entropy Metrics for Bug Prediction in Android
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32804
Reasons for Non-Applicability of Software Entropy Metrics for Bug Prediction in Android

Authors: Arvinder Kaur, Deepti Chopra

Abstract:

Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android.

Keywords: Android, bug prediction, mining software repositories, Software Entropy.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125137

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1042

References:


[1] A.E. Hassan, 2009. Predicting Faults based on complexity of code change. In the proceedings of 31st Intl. Conf. on Software Engineering. 2009. pp. 78-88.
[2] Arvinder Kaur, Kamaldeep Kaur, and Deepti Chopra."Entropy based Bug Prediction using Neural Network based regression," in Computing, Communication & Automation (ICCCA), 2015 International Conference on, vol., no., pp.168-174, 15-16 May 2015.
[3] Arvinder Kaur, Kamaldeep Kaur, and Deepti Chopra."Application of Locally Weighted Regression for Predicting Faults Using Software Entropy Metrics." In the Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 257-266. Springer India, 2016.
[4] V.B. Singh and K.K.Chaturvedi. 2012. Entropy based Bug Prediction using Support Vector Regression. In the proceedings of 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA). 2012. pp. 746-751.
[5] http://hg.mozilla.org/mozilla-central/file/9ee9e193fc48/layout/forms.
[6] https://github.com/android/platform_frameworks_base/tree/master/location.
[7] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H.: The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18. (2009).
[8] Witten, Ian H., Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
[9] Lin Tan ,Chen Liu, Zhenmin Li, Xuanhui Wang, Yuanyuan Zhou, Chengxiang Zhai. Bug Characteristics in Open Source Software.
[10] Kaur, Arvinder, and Kamaldeep Kaur. "Statistical comparison of modelling methods for software maintainability prediction." International Journal of Software Engineering and Knowledge Engineering 23.06 (2013): 743-774.
[11] Singh, Yogesh, Arvinder Kaur, and Ruchika Malhotra. "A comparative study of models for predicting fault proneness in object-oriented systems."International Journal of Computer Applications in Technology 49.1 (2014):22-41.