论文标题
权重可能有害：在基于多目标搜索的软件工程中，帕累托搜索与加权搜索
The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering
论文作者
论文摘要
在存在基于搜索的软件工程（SBSE）中优化多个目标的情况下，通常采用了Pareto搜索。它搜索了问题的帕累托最佳解决方案的良好近似值，利益相关者根据其偏好选择最喜欢的解决方案。但是，当利益相关者的明确偏好（例如，一组反映目标之间相对重要性的权重）可以在搜索之前获得，因此认为加权搜索是首选，因为它通过将原始多目标问题转换为单个目标的搜索来简化搜索，并使搜索仅仅使搜索范围仅在利益相关者感兴趣的方面，使搜索只能集中在利益方面。 本文质疑这样的“加权搜索第一”信念。我们表明，即使存在明确的偏好，重量实际上也可能对搜索过程有害。具体而言，我们进行了一项大规模的经验研究，该研究包括来自三个代表性SBSE问题的38个系统/项目，以及两种类型的搜索预算和9套权重，导致604例比较。我们的主要发现是，加权搜索在搜索初期消耗相对较少的资源来达到一定水平的解决方案质量；但是，只要我们允许足够但不切实际的搜索预算，帕累托搜索大部分时间（最多可比其加权案例的77％）要好得多。在本文中，这与其他发现和可行的建议一起，使我们能够在可用的偏好方面编写有关选择加权和帕累托搜索SBSE的务实和全面的指导。所有代码和数据均可访问：https：//github.com/ideas-labo/pareto-vs-weight-for-sbse。
In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem's Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a "weighted search first" belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.
