Hybrid Optimization of Multiple Intelligent Recommendation Engines for Software Development Cycles

Abstract

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. For example, managing collections inside bug repositories is software developers' task to fix any bugs that have been identified. Unfortunately, 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 better aims better to understand the outcomes of the latestscientific 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, selecting bug reports using hybrid machine learning methods, modeling prioritization in the bug repository, and supporting developer assignment tasks with our model. Second, we modeled 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. The introduction article, section 2 on the literature and context, section 3 on the work contribution that will be made, section 4 on the methodology analysis and the expected outcomes will be explained, section 5 on the conclusion, and finally, on the potentialaspects of this work