Context:

Software architecture refactoring can be induced by multiple reasons, such as satisfying new functional requirements or improving non-functional properties. Multi-objective optimization approaches have been widely used in the last few years to introduce automation in the refactoring process, and they have revealed their potential especially when quantifiable attributes are targeted. However, the effectiveness of such approaches can be heavily affected by configuration characteristics of the optimization algorithm, such as the composition of solutions.

Objective:

In this paper, we analyze the behavior of $EASIER$, which is an Evolutionary Approach for Software archItecturE Refactoring, while varying its configuration characteristics, with the objective of studying its potential to find near-optimal solutions under different configurations.

Method:

In particular, we use two different solution space inspection algorithms (i.e., $NSGA-II$ and $SPEA2$) while varying the genome length and the solution composition.

Results:

We have conducted our experiments on a specific case study modeled in $\text{Æmilia}$ ADL, on which we have shown the ability of $EASIER$ to identify performance-critical elements in the software architecture where refactoring is worth to be applied. Beside this, from the comparison of multi-objective algorithms, $NSGA-II$ has revealed to outperform $SPEA2$ in most of cases, although the latter one is able to induce more diversity in the proposed solutions.

Conclusion:

Our results show that the $EASIER$ thoroughly automated process for software architecture refactoring allows to identify configuration contexts of the evolutionary algorithm in which multi-objective optimization more effectively finds near-optimal Pareto solutions.

中文翻译：

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分析多目标软件架构重构对配置特征的敏感性

语境：

可以出于多种原因来诱发软件体系结构重构，例如满足新的功能要求或改善非功能特性。过去几年中，多目标优化方法已广泛用于在重构过程中引入自动化，并且它们揭示了其潜力，尤其是在针对可量化属性的情况下。但是，此类方法的有效性可能会受到优化算法的配置特征（例如解决方案的构成）的严重影响。

客观的：

在本文中，我们分析了 $E\mathrm{一个}\mathrm{小号}\mathrm{一世}E\mathrm{[R}$，这是一种用于软件架构重构的进化方法，同时改变了它的配置特征，目的是研究其在不同配置下找到接近最佳解决方案的潜力。

方法：

特别是，我们使用两种不同的解决方案空间检查算法（即 $ñ\mathrm{小号}G\mathrm{一个}-\mathrm{一世}\mathrm{一世}$ 和 $\mathrm{小号}PE\mathrm{一个}\mathrm{2个}$），同时改变基因组长度和溶液组成。

结果：

我们已经在一个以以下内容为模型的特定案例研究中进行了实验 $\text{Æ米利亚}$ ADL，我们已经证明了它的能力 $E\mathrm{一个}\mathrm{小号}\mathrm{一世}E\mathrm{[R}$以确定在软件体系结构中性能至关重要的元素，其中值得应用重构。除此之外，从多目标算法的比较来看，$ñ\mathrm{小号}G\mathrm{一个}-\mathrm{一世}\mathrm{一世}$ 表现优于大市 $\mathrm{小号}PE\mathrm{一个}\mathrm{2个}$ 在大多数情况下，尽管后一种方法能够在建议的解决方案中引入更多的多样性。

结论：

我们的结果表明 $E\mathrm{一个}\mathrm{小号}\mathrm{一世}E\mathrm{[R}$ 完全自动化的软件体系结构重构过程可以识别演化算法的配置上下文，在该上下文中，多目标优化可以更有效地找到接近最优的Pareto解决方案。