Hybrid Recommendation System Approach for appropriate developer selection in Bug Repositories

Yıl 2021, Cilt: 12 Sayı: 3, 471 - 477, 29.06.2021

Mohanad Al-imari Sefer Kurnaz Jalal S. H. Al-bayati

https://doi.org/10.24012/dumf.818164

Öz

The essential destination of this research is to develop a hybrid recommendation system methodology to enhance the overall performance accuracy of such existed systems, this recommendation approach normally utilized to assign or propose a few counted numbers of programmers or developers that capable of resolving system's bug reports generated automatically from an open source bug repository, meaning the system decides which programmers or developers should be taken into account to be in charge of finding a solution the bugs mentioned in the bug's report. The definition of the bug selection problems in bug repositories are the activities that developers achieve within program maintenance to fix some specific bugs. Because of lot of bugs are created daily, many developers required are quite large, therefore it is difficult to specify the accurate programmers or developers to find a solution for the issues for specific bug inside the code. The article also aims to improve the accuracy results obtained than existed traditional approaches for this purpose. Besides, we have considered the case of prioritization of system developers, the case can be utilized to find an appropriate grade of developers' achievements as prior knowledge to assist the system in assigning of bug report issue. The results have found that the importance of developers could support the bug triage worker more and help software tasks to solve the bugs fast and within required time during development and support cycles of the software.

Anahtar Kelimeler

Open source Bug Repositories, Hybrid machine learning approach, decision trees, Naive Bayes, Random Forest, Neural Networks, feature selection

Kaynakça

• 1. Wu, W.; Zhang, W.; Yang, Y.; Wang, Q. Time series analysis for bug number prediction. In Proceedings of the 2nd International Conference on Software Engineering and Data Mining, Chengdu, China, 23–25 June 2010, 589–596.
• 2. B.Azhagusundari; Thanamani A.S. Feature Selection based on Information Gain. IJITEE, 2013, 2, 19-21.
• 3. Xuan, J.; Jiang, H.; Ren, Z.; Zou, W. Developer prioritization in bug repositories. In Proceedings of the 2012 34th International Conference on Software Engineering (ICSE), Zurich, Switzerland, 2–9 June 2012, 25–35.
• 4. Shokripour, R.; Anvik, J.; Kasirun, Z.M.; Zammani, S. A time-based approach to automatic bug report assignment. J. Syst. Softw. 2015, 102, 109–122.
• 5. Xia, X.; Lo, D.; Ding, Y.; Al-Kofahi, J.; Nguyen, T. Improving automated bug triaging with the specialized topic model. IEEE Trans. Softw. Eng. 2016, 43, 272–297.
• 6. Ethem Alpaydin, Introduction to Machine Learning, 2nd edition,MIT press, 2010, London, England.
• 7. Anvik J.; Hiewand L.; Murphy G. Who Should Fix this Bug, ICSE, 2006, Shanghai, China, 20-28.
• 8. Breiman L. Random Forests, Springer Machine Learning, 2001, 45, 5-32.
• 9. Liuac M.; Wangb M.; Wanga J.; Lic D. ,Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar., Elsevier ,Sensors and Actuators, 2013, 177, 970–980.
• 10. Kulkarni V. Y.; Sinha P.K. Random Forest Classifiers :A Survey and Future Research Directions, International Journal of Advanced Computing, 2013, 36, 1144-1153.
• 11.Yan M.; Guo L.; Cukic B. A statistical framework for the prediction of fault-proneness." Advances in Machine Learning Applications in Software Engineering. IGI Global, 2007, 237-263.
• 12. Breiman L. OUT-OF-BAG ESTIMATION, Statistics Department, 1996, University of California, USA.
• 13. Oshiro T. M.; Perez P. S.; Baranauskas J.A. How Many Trees in a Random Forest, Department of Computer Science and Mathematics, University of Sao Paulo, Lecture Notes in Computer Science, 2012, 7376.
• 14. Amatriain X., Pompeu Fabra University. Associate Professor in Computer Science, 2019, VP of Engineering at Quora.
• 15. G. Yang, T. Zhang and B. Lee, "Towards Semi-automatic Bug Triage and Severity Prediction Based on Topic Model and Multi-feature of Bug Reports," 2014 IEEE 38th Annual Computer Software and Applications Conference, Vasteras, Sweden, 2014, pp. 97-106.