Eliciting and Using Goal Variability

引出和使用目标可变性

• 批准号: RGPIN-2014-05824
• 负责人: Liaskos, Sotirios
• 金额: $ 1.46万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611867
• 关键词: Eliciting; Using; Goal; Variability

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.

目标模型是需求工程中对涉众意图进行建模和推理的一种流行方法。他们特别建议使用在决策过程中的早期需求分析，当正确的功能集为未来的系统是寻求，并在运行时，当利益相关者的需求必须转化为运行系统的配置。在这个建议中，我们专注于沿着沿着三个方向的目标模型的研究：（a）他们的可理解性和使用的视觉工件，（B）他们从利益相关者的启发和（c）他们在软件配置和定制的利用。 关于可理解性，我们建议通过实验研究目标模型的价值，作为视觉工件，便于特设识别的最佳解决方案。我们对贡献链接特别感兴趣，目标模型中的元素代表了一个利益相关者目标的满意度如何影响另一个利益相关者的满意度。我们想研究什么样的方式来表示贡献是更适合于特设的最优解的探索。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可变性诱导分解。在贡献因素方面，我们首先提出研究假设语境和认知偏差在其引发过程中的作用。我们早期的实验表明，这些因素可以影响的有效性，引起的贡献措施。我们还建议研究的启发工具（如各种版本的定性与定量尺度）的选择在获取可靠的贡献措施的作用。此外，可变性诱导分解是引入可变（即决策）点的目标的细化。在过去，我们已经介绍了一个系统的方法来获取这样的变化点，我们现在希望经验评估的质量和完整性的变化，它的结果。 在软件配置和定制方面，我们建议继续我们目前在设计高度定制的系统以及配置现有系统方面的工作。关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源自目标和其他需求模型的策略对象行为的系统。在现有的系统，我们建议研究应用的概念建模和自动推理技术，以方便配置的大型和复杂的信息系统。我们还建议研究常见的流行系统和使用数据挖掘技术，用于识别大量用户的配置模式，旨在使用这些模式的通知和促进配置过程。 拟议的研究是针对目标建模方面，我们发现重要的是使其在行业中采用。我们相信，关于我们如何引出有效的目标模型并有效地将其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，既使定制过程更容易访问和更有效，又使软件工程师能够显着提高系统的适应性。