Exploration and analysis of alternatives is one of the main activities in requirements engineering, both in early and in late requirements phases. While i* and i* -derived modeling notations provide facilities for capturing cer- tain types of variability, domain properties (and other external influences) and their effects on i* models cannot be easily modeled. In this paper, we propose to explore how our previous work on context-dependent goal models can be ex- tended to support i* . Here, we examine how i* modeling can benefit from both monitorable (i.e., defined through real-world phenomena) and non-monitorable (e.g., viewpoints, versions, etc.) contexts defined using our context framework. 1 Introduction i* is an agent-oriented modeling framework that centers on the notions of intelligent actor and intentional dependency. The Strategic Dependency (SD) model of i* focus- es on representing the relevant actors in the organization together with their intention- al dependencies, while the Strategic Rationale (SR) model captures the rationale be- hind the processes in organizations from the point of view of participating actors. Variability in requirements and in design has been identified as crucial for develop- ing future software systems (4,5). Moreover, flexible, robust, adaptive, mobile and pervasive applications are expected to account for the properties of (as well as to adapt to changes in) their environments. Thus, modeling variability in the system environment and its effects on requirements and on other types of models is a highly desirable feature of a modeling framework. However, i* does not support capturing of how domain variations affect its diagrams. This leads to two situations. First, an over- simplification of the diagrams through the assumption of domain uniformity with the hope of producing an i* model that is adequate for the most instances of a problem. Second, the production of multiple i* models to accommodate all domain variations. The former case leads to models that fail to account for the richness of domain varia- tions, while the latter introduces serious model management problems due to the need to oversee large numbers of models as well as to capture their relationships. In this paper, we adapt the ideas from (3) to the i* modeling framework and propose an ap- proach that uses contexts to structure domain variability and to concisely represent and analyze the variations in i* models resulting from this domain variability as well as from other external factors such as viewpoints, etc. Using the proposed approach, CEUR Proceedings of the 5th International i* Workshop (iStar 2011)
['Alexei Lapouchnian', 'John Mylopoulos']
Capturing Contextual Variability in i* Models