While working on a software specification, designers usually need to evaluate
different architectural alternatives to be sure that quality criteria are met.
Even when these quality aspects could be expressed in terms of multiple
software metrics, other qualitative factors cannot be numerically measured, but
they are extracted from the engineer's know-how and prior experiences. In fact,
detecting not only strong but also weak points in the different solutions seems
to fit better with the way humans make their decisions. Putting the human in
the loop brings new challenges to the search-based software engineering field,
especially for those human-centered activities within the early analysis phase.
This paper explores how the interactive evolutionary computation can serve as a
basis for integrating the human's judgment into the search process. An
interactive approach is proposed to discover software architectures, in which
both quantitative and qualitative criteria are applied to guide a
multi-objective evolutionary algorithm. The obtained feedback is incorporated
into the fitness function using architectural preferences allowing the
algorithm to discern between promising and poor solutions. Experimentation with
real users has revealed that the proposed interaction mechanism can effectively
guide the search towards those regions of the search space that are of real
interest to the expert.

通过交互式进化计算以及应用定量和定性评判标准指导多目标进化算法，将用户反馈嵌入到适应函数中，使算法能够辨别出有前景和较差的解决方案，实验证明该交互机制可以有效地引导搜索进入专家真正感兴趣的搜索空间区域。

软件架构的交互式多目标进化优化

Interactive Multi-Objective Evolutionary Optimization of Software  Architectures

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:
this https URL.

本文探讨了在 Search-Based Software Engineering (SBSE) 中，当清晰的利益相关者偏好给定时，是否应该优先使用加权搜索，结果表明，即使在存在明确偏好的情况下，权重也可能对搜索进程有害。具体来说，作者进行了大规模的实证研究，结果在通常搜索预算的情况下表明，帕累托搜索在绝大多数情况下，甚至达到 77％的情况下比其加权对应物有显着优势。