SavedAdd your journal

Journals / Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi / 2021 / Cilt: 12 - Sayı: 3

Hybrid Recommendation System Approach for appropriate developer selection in BugRepositories

Sefer KURNAZ
Mohanad AL-IMARI
Jalal S. H. AL-BAYATI
Pages
471–477
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The essential destination of this research is to develop a hybrid recommendation system methodology toenhance the overall performance accuracy of such existed systems, this recommendation approachnormally utilized to assign or propose a few counted numbers of programmers or developers that capableof resolving system's bug reports generated automatically from an open source bug repository, meaningthe system decides which programmers or developers should be taken into account to be in charge offinding a solution the bugs mentioned in the bug's report.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The essential destination of this research is to develop a hybrid recommendation system methodology toenhance the overall performance accuracy of such existed systems, this recommendation approachnormally utilized to assign or propose a few counted numbers of programmers or developers that capableof resolving system's bug reports generated automatically from an open source bug repository, meaningthe system decides which programmers or developers should be taken into account to be in charge offinding a solution the bugs mentioned in the bug's report.
📊
Key Findings & Data:Because of lot of bugs are created daily, many developers required are quite large, therefore it isdifficult to specify the accurate programmers or developers to find a solution for the issues for specificbug inside the code.
💡
Conclusion & Impact:The results have found that the importance of developerscould support the bug triage worker more and help software tasks to solve the bugs fast and within requiredtime during development and support cycles of the software.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The essential destination of this research is to develop a hybrid recommendation system methodology toenhance the overall performance accuracy of such existed systems, this recommendation approachnormally utilized to assign or propose a few counted numbers of programmers or developers that capableof resolving system's bug reports generated automatically from an open source bug repository, meaningthe system decides which programmers or developers should be taken into account to be in charge offinding a solution the bugs mentioned in the bug's report. The definition of the bug selection problems inbug repositories are the activities that developers achieve within program maintenance to fix some specificbugs. Because of lot of bugs are created daily, many developers required are quite large, therefore it isdifficult to specify the accurate programmers or developers to find a solution for the issues for specificbug inside the code. The article also aims to improve the accuracy results obtained than existed traditionalapproaches 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 toassist the system in assigning of bug report issue. The results have found that the importance of developerscould support the bug triage worker more and help software tasks to solve the bugs fast and within requiredtime during development and support cycles of the software.

🔖

Cite This Article

KURNAZ, S., AL-IMARI, M., AL-BAYATI, J. S. H. (2022). Hybrid Recommendation System Approach for appropriate developer selection in BugRepositories. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, Cilt: 12 - Sayı: 3, 471–477. https://ajindex.com/articles/487486

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  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
  11. Breiman L. OUT-OF-BAG ESTIMATION, Statistics Department, 1996, University of California, USA
  12. 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
  13. Amatriain X., Pompeu Fabra University. Associate Professor in Computer Science, 2019, VP of Engineering at Quora
  14. G. Yang, T. Zhang and B. Lee, "Towards Semiautomatic 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.