Towards anticipation of architectural smells using link prediction techniques

ANDRES DIAZ PACE; ANTONELA TOMMASEL; DANIELA GODOY

Madrid

Conferencia; 18th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2018); 2018

Software systems naturally evolve, and this evolutionoften brings design problems that cause system degradation.Architectural smells are typical symptoms of such problems, andseveral of these smells are related to undesired dependenciesamong modules. The early detection of these smells is importantfor developers, because they can plan ahead for maintenance orrefactoring efforts, thus preventing system degradation. Existingtools for identifying architectural smells can detect the smellsonce 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 groups of likely module dependencies that can anticipate architectural smells in a future system version. 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 dependency-based smells or metrics.