BackgroundRelease planning (RP) is one of the most complex and relevant activities in the iterative and incremental software development, because it addresses all decisions associated with the selection and assignment of requirements to releases. There are many approaches in which RP is formalized as an optimization problem. In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. Since RP is a wicked problem with a large focus on human intuition, the decision maker’s (DM) opinion is a relevant issue to be considered when solving release planning problem. Thus, we emphasize the importance in gathering the DM’s preferences to guide the optimization process through search space area of his/her interests.MethodsTypically, RP is modelled as a multi-objective problem by considering to maximize overall clients satisfaction and minimize project risk. In this paper, we extend this notion and consider DM’s preferences as an additional objective. The DM defines a set of preferences about the requirements allocation which is stored in a preference base responsible for influencing the search process. The approach was validated through an empirical study, which consists of two different experiments, respectively identified as (a) automatic experiment and (b) participant-based experiment. Basically, the former aims to analyze the approach using different search-based algorithms (NSGA-II, MOCell, IBEA, and SPEA-II), over artificial and real-world instances, whereas the latter aims at evaluating the use of the proposal in a real scenario composed of human evaluations.ResultsThe automatic experiment points out that NSGA-II obtained overall superiority in two of the three datasets investigated, positioning itself as a superior search technique for scenarios with few number of requirements and preferences, while IBEA showed to be better for larger ones (with more requirements and preferences). Regarding the participant-based experiment, it was found that two thirds of the participants evaluated the preference-based solution better than the non-preference-based one.ConclusionsThe results suggest that it is feasible to investigate the approach in a real-world scenario. In addition, we made available a prototype tool in order to incorporate the human’s preferences about the requirements allocation into the solution of release planning.

中文翻译：

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在软件发布计划的多目标方法中结合决策者的偏好

背景发布计划 (RP) 是迭代和增量软件开发中最复杂和最相关的活动之一，因为它涉及与选择和发布需求分配相关的所有决策。有许多方法将 RP 形式化为优化问题。在这种情况下，基于搜索的软件工程 (SBSE) 处理应用搜索技术来解决软件工程的复杂问题。由于 RP 是一个严重关注人类直觉的问题，决策者 (DM) 的意见是解决发布计划问题时要考虑的相关问题。因此，我们强调收集 DM 的偏好以通过他/她感兴趣的搜索空间区域指导优化过程的重要性。通过考虑最大化整体客户满意度和最小化项目风险，RP 被建模为一个多目标问题。在本文中，我们扩展了这一概念，并将 DM 的偏好作为附加目标。DM 定义了一组关于需求分配的偏好，这些偏好存储在负责影响搜索过程的偏好库中。该方法通过实证研究得到验证，该研究由两个不同的实验组成，分别确定为（a）自动实验和（b）基于参与者的实验。基本上，前者旨在在人工和真实世界实例上使用不同的基于搜索的算法（NSGA-II、MOCell、IBEA 和 SPEA-II）分析该方法，而后者旨在评估该提案在由人工评估组成的真实场景。结果自动实验指出，NSGA-II 在所调查的三个数据集中的两个数据集中获得了整体优势，将自己定位为一种适用于需求和偏好数量很少的场景的优越搜索技术，而 IBEA 对于较大的场景（具有更多需求）表现得更好和偏好）。关于基于参与者的实验，发现三分之二的参与者对基于偏好的解决方案的评价优于非基于偏好的解决方案。结论结果表明，在现实世界中研究该方法是可行的。此外，我们提供了一个原型工具，以便将人们对需求分配的偏好纳入发布计划的解决方案中。