一句话：提出基于聚类的多目标搜索，以降低交互式重构中开发者评估和调整推荐的工作量。
重构在工业界被广泛采用，因为糟糕的设计决策代价高且风险大。自动重构不一定产生期望设计，手工重构则易错、耗时且难以应对激进改动。近期研究将开发者反馈引入自动重构推荐，因为开发者更理解问题域并可能已有目标设计。但该交互过程往往重复、昂贵且繁琐：开发者必须评估推荐重构并使其适配目标设计，尤其在大型系统中可能策略数量呈指数增长。为此，论文提出基于聚类的多目标搜索方法，以降低交互式重构所需的工作量。摘要未报告具体实验结果。
Refactoring is nowadays widely adopted in the industry because bad design decisions can be very costly and extremely risky. On the one hand, automated refactoring does not always lead to the desired design. On the other hand, manual refactoring is error-prone, time-consuming and not practical for radical changes. Thus, recent research trends in the field focused on integrating developers feedback into automated refactoring recommendations because developers understand the problem domain intuitively and may have a clear target design in mind. However, this interactive process can be repetitive, expensive, and tedious since developers must evaluate recommended refactorings, and adapt them to the targeted design especially in large systems where the number of possible strategies can grow exponentially.
DOI 原文 ·
@inproceedings{paperbot4736,
title = {Reducing interactive refactoring effort via clustering-based multi-objective search},
author = {Vahid Alizadeh and Marouane Kessentini},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3238217}
}