A partition-based algorithm for clustering large-scale software systems

Clustering techniques are used to extract the structure of software for understanding, maintaining, and refactoring. In the literature, most of the proposed approaches for software clustering are divided into hierarchical algorithms and search-based techniques. In the former, clustering is a process of merging (splitting) similar (non-similar) clusters. These techniques suffered from the drawbacks such as finiteness criterion and arbitrary decisions occurred in the process. Because of the NP-hardness of clustering software systems, evolutionary and search-based algorithms are more commonly used algorithm than hierarchical ones. In evolutionary algorithms, the clustering of software systems is considered as a problem of searching over some possible clustering candidates. Although these algorithms are often able to achieve an appropriate structure of the software, they are not applicable in clustering large-scale software. Furthermore, these algorithms are unable to consider the knowledge in the artifact dependency graph, which extracted from the source code of the software. In software systems, an artifact can be everything like a class, a function, or a file. In this paper, a new partition-based clustering algorithm is presented. This algorithm attempts to partition the artifact dependency graph considering the knowledge therein. Moreover, a new distance criterion is presented to measure the similarity and dissimilarity of the artifacts. The proposed algorithm starts with the artifact dependency graph and creates the similarity matrices of the artifacts. So, it attempts to refine the partition candidate until a fixed point is reached. We expect that the proposed method compared with other methods could lead to achieve the clustering with high quality and similar to the expert's clustering based on MoJo-FM measure. To demonstrate the applicability and validity of the proposed algorithm, a large-scale case study, Mozilla Firefox, is employed. The results demonstrate that the proposed algorithm outperforms the commonly used evolutionary methods in the literature.