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.

翻译：软件模型优化是一种自动生成设计备选方案的过程，旨在提升软件系统的可量化非功能属性，如性能与可靠性。多目标进化算法能有效辅助设计者在期望的非功能属性之间权衡取舍。为减少计算资源消耗，本研究探讨了实施搜索预算以限制设计备选方案搜索范围的影响。具体而言，我们通过运用质量指标并分析生成设计备选方案的结构特征，探究时间预算如何影响帕累托前沿的质量。本研究发现，当实施搜索预算时，不同进化算法表现出显著的行为差异；进一步揭示，在预算约束下生成的设计备选方案与无预算条件下产生的方案存在结构差异。此外，我们为设计者提供了关于时间约束下算法选择的建议，从而促进自动化重构技术在改善非功能属性方面的有效应用。