发表机构
ISISTAN, CONICET-UNCPBA; SPENCER Lab, University of L’Aquila; Johannes Kepler University Linz（ISISTAN, 阿根廷国家科学研究委员会-内格罗河省大学; 拉奎拉大学SPENCER实验室; 林茨约翰·开普勒大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于回归的代理模型替代昂贵分析工具，加速多目标架构重构优化，节省高达30%计算时间并保持帕累托前沿质量。
AI 中文摘要
软件模型优化是一个生成架构替代方案的过程，旨在改善软件系统的可量化非功能属性，如性能和可靠性。多目标进化算法通常用于探索搜索空间，并帮助设计者在相互竞争的非功能属性之间识别权衡（例如，通过帕累托前沿）。然而，此类算法在复杂软件模型和大型设计空间中面临效率挑战，因为在搜索过程中反复调用评估每个架构适应度（即质量）所需的分析工具时，这些工具的计算成本变得很高。在本文中，我们探索了基于回归技术的代理模型构建方法，以显著降低的计算成本近似这些分析工具的输出，同时保持合理的输出精度。我们的实验结果表明，代理模型在计算时间上节省了高达30%，并保持了进化算法提供的帕累托前沿质量。此外，我们观察到我们方法生成的架构模型存在一些差异。总体而言，代理模型是将多目标架构优化扩展到更大空间和复杂架构模型的一种有前景的方法。
英文摘要
Software model optimization is a process that generates architecture alternatives aimed at improving quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms are commonly used to explore the search space and help designers identify trade-offs among competing non-functional properties (e.g., through a Pareto front). However, such algorithms face efficiency challenges in complex software models and large design spaces, since evaluating the fitness (i.e., the quality) of each architecture requires analysis tools that become computationally expensive when repeatedly invoked during the search process. In this paper, we explore the construction of surrogate models based on regression techniques to approximate the outputs of these analysis tools at significantly lower computational cost, while maintaining reasonable output accuracy. Our experimental results suggest that surrogate models provide savings of up to $30\%$ in computational time and maintain the Pareto front quality provided by evolutionary algorithms. Also, we observed some differences in the architectural models produced by our approach. Overall, surrogate models constitute a promising approach for scaling multi-objective architecture optimization to larger spaces and complex architectural models.
