Systematic Literature Review on the Use of Cluster Analysis in Software Design Activities

Cluster analysis is an unsupervised machine learning approach that groups data into homogeneous categories without the need for predefined labels. Although it was not originally developed for software engineering, this technique has increasingly been applied to support various activities in the software design phase. However, information about its use remains scattered across different studies. To address this gap, this work presents a systematic literature review synthesizing the state of the art on the application of cluster analysis in software design. Following a rigorous selection process, 14 primary studies published between 2019 and 2025 were identified from four digital libraries: IEEE Xplore, ACM Digital Library, Springer Link, and ScienceDirect. This review highlights the contexts in which clustering has been applied, emphasizing its predominant role in class decomposition tasks and the frequent adoption of the K-means algorithm, while also documenting the algorithms and tools used during design activities. Furthermore, the analysis discusses the benefits and challenges of adopting cluster analysis in this stage of development. The findings provide software engineering researchers and practitioners with a consolidated overview of the role of cluster analysis in software design, offering insights into its potential, limitations, and directions for future research and practice. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1262Dimensions.Open Alex.