Hierarchical Clustering for Software Architecture Recovery

TLDR

Understanding a software system’s architecture is essential, and when no description exists, researchers use clustering of source code to recover it. The paper reviews hierarchical clustering for architecture recovery, analyzes similarity measures, evaluates clustering algorithms, and compares two recent methods. The authors review hierarchical clustering literature, examine domain‑specific characteristics of similarity measures and clustering algorithms, and analyze how algorithmic decisions influence clustering quality. Experiments on four legacy systems reveal how well‑known clustering algorithms behave and their characteristics in the software domain.

Abstract

Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main contributions of this paper are given as follows. First, we review hierarchical clustering research in the context of software architecture recovery and modularization. Second, to employ clustering meaningfully, it is necessary to understand the peculiarities of the software domain, as well as the behavior of clustering measures and algorithms in this domain. To this end, we provide a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering. Third, we analyze the clustering process of various well-known clustering algorithms by using multiple criteria, and we show how arbitrary decisions taken by these algorithms during clustering affect the quality of their results. Finally, we present an analysis of two recently proposed clustering algorithms, revealing close similarities in their apparently different clustering approaches. Experiments on four legacy software systems provide insight into the behavior of well-known clustering algorithms and their characteristics in the software domain.