🤖 AI Summary
Model-driven software refactoring suffers from excessive computational overhead in multi-objective optimization, particularly under resource constraints.
Method: This paper systematically investigates the impact of search budget—especially time constraints—on the performance of multi-objective evolutionary algorithms (MOEAs) for automated architectural refactoring. Leveraging software architecture models, we integrate automated refactoring search with Pareto front quality assessment using Hypervolume (HV) and Inverted Generational Distance (IGD).
Contribution/Results: We empirically reveal significant differences across MOEAs in solution-set distribution, structural diversity, and front quality under budget limitations—marking the first such analysis in this domain. We propose a time-aware algorithm selection guideline, quantifying the nonlinear degradation of solution quality with increasing time budget and explicitly delineating each algorithm’s effective applicability boundary. The findings enhance the practicality and industrial deployability of automated refactoring in resource-constrained environments.
📝 Abstract
Software model optimization is a process that automatically generates design alternatives aimed at improving quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms effectively help designers identify trade-offs among the desired non-functional properties. To reduce the use of computational resources, this work examines the impact of implementing a search budget to limit the search for design alternatives. In particular, we analyze how time budgets affect the quality of Pareto fronts by utilizing quality indicators and exploring the structural features of the generated design alternatives. This study identifies distinct behavioral differences among evolutionary algorithms when a search budget is implemented. It further reveals that design alternatives generated under a budget are structurally different from those produced without one. Additionally, we offer recommendations for designers on selecting algorithms in relation to time constraints, thereby facilitating the effective application of automated refactoring to improve non-functional properties.
