ISISTAN   23985
INSTITUTO SUPERIOR DE INGENIERIA DEL SOFTWARE
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Towards Anticipation of Architectural Smells using Link Prediction Techniques
Autor/es:
J. ANDRÉS DÍAZ PACE; ANTONELA TOMMASEL; DANIELA GODOY
Lugar:
Madrid
Reunión:
Conferencia; 18th IEEE International Working Conference on Source Code Analysis and Manipulation; 2018
Institución organizadora:
IEEE
Resumen:
Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies among modules. The early detection of these smells is important for developers, because they can plan ahead for maintenance or refactoring efforts, thus preventing system degradation. Although there are tools for identifying architectural smells, they can detect the smells once they exist in the source code. This means that their undesired dependencies are already created. In this work, we explore a forward-looking approach that is able to infer likely module dependencies that can anticipate architectural smells in future system versions. Our approach considers the current module structure as a network, along with information from previous versions, and applies link prediction techniques (from the field of social network analysis). In particular, we focus on dependency-related smells, such as Cyclic Dependency and Hub-like Dependency, which fit well with the link prediction model. An initial evaluation with two open-source projects shows that, under certain considerations, the predictions of our approach are satisfactory. Furthermore, the approach can be extended to other types of architectural smells.