Yazılım geliştirme döngüleri için çoklu akıllı öneri motorlarının hibrit optimizasyonu

The primary purpose is to create a hybrid recommendation system approach to improve the performance of such systems. This recommendation system would typically be used to assign or suggest a small number of developers suitable for troubleshooting a bug report. Managing collections inside bug repositories is software developers' task to fix any particular bugs that have been identified. Bugs are often created, so the number of developers needed is high, so it's hard to decide how many to assign to specific tasks. This analysis aims better to understand the outcomes of the latest scientific methods. We also addressed developer prioritization and how it can be used to determine the assignment of a problem to a developer. We have studied two aspects: first, the selection of bug reports using hybrid machine learning methods, and modelling developer prioritization in the bug repository and supporting developer assignment tasks with our model. Second, we modelled the relevant objectives suggested by the developers' backgrounds based on proven knowledge and experience. The study focuses on two topers' experience with fixing bugs and developer rankings in the App Store. We've tried to take better assignments using developer prioritization in bug repositories, e.g., bug triage, severity identification and re-opened bug prediction. We examine the output of the model in a representative sample of bug repositories. The results show that the prioritization of developers' prioritization triage worker and allow the program to solve the bugs more effectively in support of the software support has been clarified. After the introduction, Chapter 2 relates the literature; chapter 3 is about the methodologies used in studies, the findings and explanation are described in this thesis, chapter 4, the following chapter is the description accompanied by references.