Des signatures numériques pour améliorer la recherche structurelle de patrons

Abstract

Design patterns describe good solutions to common and recurring problems in program design. The solutions are design motifs, which software engineers introduce in the architecture of their programs. It is important to identify the design motifs used in a program architecture to understand solved design problems and to make informed changes to the program, during maintenance. The identification of micro-architectures similar to design motifs is difficult because of the large search space, i.e., the many possible combinations of classes. We propose an experimental study of classes playing roles in design motifs using metrics and a machine-learning algorithm to associate numerical signatures with design motifs roles. A numerical signature is a set of metric values characterising classes playing a given role. We devise numerical signatures experimentally using a repository of micro-architectures similar to design motifs. We show that numerical signatures help in reducing the search space of micro-architectures similar to design motifs efficiently using the Composite design motif and the JHOTDRAW framework.