Enhancement of Recommendation Engine Technique for Bug System Fixes

This study aims to develop a recommendation engine methodology to enhance the model's effectiveness and efficiency. The proposed model is commonly used to assign or propose a limited number of developers with the required skills and expertise to address and resolve a bug report. Managing collections within bug repositories is the responsibility of software engineers in addressing specific defects. Identifying the optimal allocation of personnel to activities is challenging when dealing with software defects, which necessitates a substantial workforce of developers. Analyzing new scientific methodologies to enhance comprehension of the results is the purpose of this analysis. Additionally, developer priorities were discussed, especially their utility in allocating a problem to a specific developer. An analysis was conducted on two key areas: first, the development of a model to represent developer prioritizing within the bug repository, and second, the use of hybrid machine learning techniques to select bug reports. Moreover, we use our model to facilitate developer assignment responsibilities. Moreover, we considered the developers' backgrounds and drew upon their established knowledge and experience when formulating the pertinent objectives. An examination of two individuals' experiences with software defects and how their actions impacted their rankings as developers in a software project is presented in this study. Researchers are implementing developer categorization techniques, assessing severity, and reopening bugs. A suitable number of bug reports is used to examine the model's output. A developer's bug assignment employee has been established, enabling the program to successfully address software maintenance issues with the highest accuracy of 78.38%. Best engine performance was achieved by optimizing and cleansing data, using relevant attributes, and processing it using deep learning.