Software module clustering: An in-depth literature analysis

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field

An approach for measuring the software modularity based on the bursty evolution of functional dependencies

Modular Design of a software system is one of the parameters which defines the complexity of a software system. If the software is built as one whole module, then it makes testing a long process. Also, updating the software will make a significant impact on the whole system code because of the dependencies. We propose a methodology to study and visualize the evolution of the modular structure of a network of functional dependencies in a software system. We used the Understand C++ tool for analyzing the dependencies and Gephi to produce the network. Our method analyzes the modularity of the software and identifies specific periods of significant activities, which are known as the evolutionary hot spots in software systems. As a case study, we analyzed the modular structure of Octave during its life cycle beginning from 1993 to the present