Applying Graph Partitioning-Based Seeding Strategies to Software Modularisation

Abstract

Software modularisation is a pivotal facet within software engineering, seeking to optimise the arrangement of software components based on their interrelationships. Despite extensive investigations in this domain, particularly concerning evolutionary computation, the research emphasis has transitioned towards solution design and convergence analysis rather than pioneering methodologies. The primary objective is to attain efficient solutions within a pragmatic timeframe. Recent research posits that initial positions in the search space wield minimal influence, given the prevalent trend of methods converging upon akin local optima. This paper delves into this phenomenon comprehensively, employing graph partitioning techniques on dependency graphs to generate initial clustering arrangement seeds. Our empirical discoveries challenge conventional insight, underscoring the pivotal role of seed selection in software modularisation to enhance overall outcomes.