Abstract
The information technology sector is continuously growing, and there is a high demand for developers. In the area of software development projects, fixing bugs or solving issues is a task that could be optimized to improve the productivity of developers. Making an adequate allocation for bug fixing will save overall project development time. Moreover, the problem will last for the shortest possible time, minimizing any negative impacts in case the project is already in production. This research work’s objective is to identify the most apt users (where the term “user” refers to any technology professional, for example a software developer, who has registered on any given platform), from a set of different user profiles, for fixing bugs in a software project. The study has been carried out by analyzing large-scale repositories of open-source projects with a large historical volume of bugs, and the extracted knowledge has been successfully applied to new, unrelated projects. Different similarity-based profile raking procedures have been studied, including neural-network-based incidence representation. The obtained results show that the system can be directly applied to different environments and that the selected user profiles are very close to those selected by human experts, which demonstrates the correct functioning of the proposed system.
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