Recommendation of technological profiles to collaborate in software projects using document embeddings
- PDF / 551,948 Bytes
- 8 Pages / 595.276 x 790.866 pts Page_size
- 4 Downloads / 150 Views
(0123456789().,-volV)(0123456789(). ,- volV)
S.I. : WORLDCIST’20
Recommendation of technological profiles to collaborate in software projects using document embeddings Pablo Chamoso1 • Guillermo Herna´ndez2 • Alfonso Gonza´lez-Briones3 • Francisco J. Garcı´a-Pen˜alvo4 Received: 17 July 2020 / Accepted: 10 November 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
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 opensource 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. Keywords Text analysis Artificial Neural Networks Large-scale repositories Candidate selection Software bugs Solving software issues
1 Introduction
& Francisco J. Garcı´a-Pen˜alvo [email protected] Pablo Chamoso [email protected] Guillermo Herna´ndez [email protected] Alfonso Gonza´lez-Briones [email protected] 1
BISITE Research Group, University of Salamanca, Calle Espejo, 24.2, Salamanca, Spain
2
AIR Institute, Paseo de Bele´n 11, Campus Miguel Delibes, 47011 Valladolid, Spain
3
GRASIA Research Group, Complutense University of Madrid, 28040 Madrid, Spain
4
GRIAL Research Group. Department of Computer Science and Automation, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
The most recent advances in computer science have made it possible to automate a large number of tasks in different job sectors. The increase in the processing capacity of computers and the techniques that allow for the execution of tasks on different machines (distributed computing or resource virtualization) have, over the last few years, increased the capacities of methodologies based on Artificial Intelligence (AI). One of the job sectors where AI-based methodologies have much to contribute is precisely the technology sector. This is a sector with a high employability rate; the
Data Loading...
