Eliciting and Using Goal Variability

目标模型是在需求工程中对涉众意图进行建模和推理的流行方法。在早期需求分析期间，当为将来的系统寻找正确的功能集时，以及在运行时，当涉众的需求必须转换为运行系统的配置时，它们都被特别地建议用于决策制定。在本提案中，我们重点研究目标模型的三个方向：(A)它们的可理解性和作为视觉产物的使用；(B)它们从利益相关者那里获得的启发；以及(C)它们在软件配置和定制中的应用。 在可理解性方面，我们建议通过实验研究目标模型作为视觉人工制品的价值，以促进对最优解的特别识别。我们对贡献链接特别感兴趣，目标模型中的元素表示一个涉众目标的满意度如何影响另一个涉众目标的满意度。我们想要研究用什么方式来表示贡献更适合于对最优解的特别探索。我们还建议将目标模型与其他决策支持可视化进行比较，并研究与我们过去提出的自动推理工具的交互是否有助于理解。 关于启发，我们感兴趣的是目标模型的两个基本组成部分是如何产生的：前面提到的贡献元素和引起变异性的分解。在贡献因素方面，我们首先提出要研究假设语境和认知偏差在其诱导过程中所起的作用。我们的早期实验表明，这些因素会影响所引发的贡献措施的有效性。我们还建议研究启发式工具的选择(例如，不同版本的定性和定量量表)在获得可靠的贡献度量方面的作用。此外，变异性诱导分解是对引入变化点(即决策点)的目标的细化。在过去，我们已经引入了一种系统的方法来获取这样的变化点，现在我们希望根据它所产生的可变性的质量和完整性对其进行经验评估。 在软件配置和定制方面，我们提议继续我们目前在设计高度可定制的系统以及配置现有系统方面的工作。对于设计高度可定制的系统，我们建议扩展我们早期在基于策略的行为定制方面的工作，并定义更严格的方法来开发根据直接从目标和其他需求模型派生的策略对象的系统。在现有系统方面，我们建议研究概念建模和自动推理技术的应用，以便于大型复杂信息系统的配置。我们还建议研究常见的流行系统和使用数据挖掘技术来识别大量用户中的配置模式，目的是使用这些模式来通知和促进配置过程。 拟议的研究针对的是目标建模的一些方面，我们认为这些方面对于使其能够在行业中采用很重要。我们相信，关于我们如何得出有效的目标模型并有效地将它们用于问题探索和理解的具体证据，将使它们对实践者更具吸引力。同样，引入基于需求的工具集来定义和控制软件系统中的可变性，既可以使定制过程更易于访问和高效，也可以使软件工程师显著提高其系统的适应性。

Goal models are a popular approach for modeling and reasoning about stakeholder intentions in requirements engineering. They have particularly been proposed for use in decision making both during early requirements analysis, when the right set of features for a future system is sought, and at run-time, when stakeholder needs must translate into configurations of a running system. In this proposal we focus on the study of goal models along three directions: (a) their comprehensibility and use as visual artifacts, (b) their elicitation from stakeholders and (c) their utilization in software configuration and customization. With respect to comprehensibility we propose to experimentally study the value of goal models as visual artifacts that facilitate ad-hoc identification of optimal solutions. We are particularly interested in contribution links, elements in the goal models that represent how satisfaction of one stakeholder goal influences satisfaction of the other. We want to study what ways to represent contributions is more suitable for ad-hoc exploration of optimal solutions. We also propose to compare goal models with other decision support visualizations as well as to study whether interaction with automated reasoning tools we have proposed in the past facilitates comprehension. With respect to elicitation, we are interested in how two basic components of goal models come about: the aforementioned contribution elements and variability-inducing decompositions. In terms of contribution elements, we firstly propose to study the role of assumed context and cognitive biases during their elicitation. Our early experiments suggest that such factors can influence the validity of the elicited contribution measures. We also propose to study the role of the choice of elicitation instrument (e.g. various versions of qualitative vs. quantitative scales) in acquiring reliable contribution measures. Variability-inducing decompositions, moreover, are refinements of goals that introduce variation (i.e. decision) points. In the past we have introduced a systematic approach to acquiring such variation points, which we now wish to empirically evaluate in terms of the quality and completeness of the variability it results to. In terms of software configuration and customization, we propose to continue our current work on the design of highly-customizable systems as well as the configuration of existing systems. With respect to designing highly-customizable systems we propose to extend our earlier work on policy-based behavioral customization and define more rigorous ways for developing systems that behave according to policy objects that are directly derived from goal and other requirements models. In terms of existing systems, we propose to study application of conceptual modeling and automated reasoning techniques in order to facilitate configuration of large and complex information systems. We also propose to study common popular systems and the use of data mining techniques for identifying configuration patterns within large numbers of users, aiming at using those patterns for informing and facilitating the configuration process. The proposed research is targeted towards aspects of goal modeling that we find important for enabling its adoption in the industry. We believe that concrete evidence on how we can elicit valid goal models and use them effectively for problem exploration and understanding will make them more appealing to practitioners. Likewise, introducing requirements-based toolsets for defining and controlling variability in software systems both makes the customization process more accessible and efficient and allows software engineers to dramatically increase the adaptability of their systems.