Software Module Clustering: An In-Depth Literature Analysis

TL;DR

This paper provides a comprehensive review of 143 research papers on software module clustering, analyzing methods, applications, challenges, and research gaps to guide future research in this field.

Contribution

It offers an in-depth literature analysis of software module clustering, summarizing state-of-the-art approaches, challenges, and research gaps based on extensive review.

Findings

Reviewed 143 research papers on software module clustering

Identified key clustering algorithms and evaluation methods

Discussed research gaps and future challenges

Abstract

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.