Conclusions
In this piece of research, we have presented a semantic-based system that is able to suggest which personnel could be incorporated into a new software project by considering their experience on previous projects. Our proposal obtained encouraging results with F-measure scores of 0.7131 (P60), 0.7588 (P80), and 0.6971 (P90) for similarity projects and precision scores of 0.6127 (P60) and 0.7382 (P80 and P90) for personnel suggestion. The main contribution of our research effort is twofold. First, we propose an ontology focused on describing the competences and experience of the personnel within an organization. Second, the integration of semantic techniques to the personnel assignment process within a software development organization can help the human resources manager to speed up this task, and even make it completely automated. And all of this in addition to a high level of confidence that the personnel selected by the system are the most appropriate for the project. Moreover, the method proposed herein can be applied to the organization’s current software development process without any changes having to be made to it. Furthermore, our approach has been designed to be domain-independent, i.e. our system can be implemented in different organizations with the only requirement that the system be provided with an ontology that models the corresponding context. It is important to stress that the incorporation of semantic technologies into the project helps to improve data quality and consistency, thus allowing this information to be used for other purposes, since one of the main goals of the semantic web, and more specifically of ontologies, is for them to serve as a reference for communication not only among humans but also among computers.
