Eliciting and Using Goal Variability

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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.