DCT: An scalable multi-objective module clustering tool

• Main Authors: TARCHETTI, Ana Paula M., AMARAL, Luis Henrique Vieira, OLIVEIRA, Marcos C., BONIFACIO, Rodrigo, PINTO, Gustavo, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Genetic Algorithms; Multi-Objective optimization; Software Module Clustering; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5644; https://ink.library.smu.edu.sg/context/sis_research/article/6647/viewcontent/scam2020_DCT_tool_av.pdf
• Institution: Singapore Management University

Maintaining complex software systems is a timeconsuming and challenging task. Practitioners must have a general understanding of the system’s decomposition and how the system’s developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 years, there are just a few tools publicly available. Still, these available tools do not scale to large scenarios, in particular, when optimizing multi-objectives. In this paper we present the Draco Clustering Tool (DCT), a new software module clustering tool. DCT design decisions make multi-objective software clusterization feasible, even for software systems comprising up to 1,000 modules. We report an empirical study that compares DCT with other available multi-objective tool (HD-NSGA-II), and both DCT and HD-NSGA-II with mono-objective tools (BUNCH and HD-LNS). We evidence that DCT solves the scalability issue when clustering medium size projects in a multi-objective mode. In a more extreme case, DCT was able to cluster Druid (an analytics data store) 221 times faster than HD-NSGA-II.