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Modeling and reasoning about uncertainty in goal models: a decision-theoretic approach

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Abstract

Goal models have been a popular subject of study by researchers in requirements engineering, due to their ability to capture and analyze alternative solutions through which a software system can achieve business objectives. A plethora of analysis methods for automated identification of optimal alternatives have been proposed. However, such methods often assume an idealized reality where all tasks are successfully performed when attempted and all goals are eventually satisfied with certainty when pursued according to a solution. In reality, some tasks run the risk of failure while others produce chance outcomes. In this paper, we extend the standard goal modeling language to allow representation and reasoning about both uncertainty and preferential utility in goals. Tasks are extended to allow for probabilistic effects and preferential statements of stakeholders are captured and translated into utilities over possible effects. Moreover, solutions are not mere specifications (functions, quality constraints, and assumptions), but rather policies, that is sequences of situational action decisions, through which stakeholder goals can be fulfilled. An AI reasoning tool is adapted and used for identifying optimal policies with respect to the value they offer to stakeholders measured against their probability of failure. Evaluation of the approach includes a simulation study and scalability experiments to assess the applicability of automated reasoning for larger problems.

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Notes

  1. DT-Golog’s definition of a policy is slightly different from the usual concept of a nonstationary Markov policy [50], which is a function mapping each state and a decision epoch to an action. In particular, DT-Golog policies prescribe an action only in those states that are reachable from the initial state (that corresponds to the initial situation \(S_0\)).

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Authors and Affiliations

  1. School of Information Technology, York University, 4700 Keele Street, Toronto, ON, Canada

    Sotirios Liaskos

  2. Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, SK, Canada

    Shakil M. Khan

  3. Department of Computer Science, University of Toronto, 214 College St, Toronto, ON, Canada

    John Mylopoulos

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  1. Sotirios Liaskos
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  2. Shakil M. Khan
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Correspondence to Sotirios Liaskos.

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Communicated by J. Araujo, A. Moreira, G. Mussbacher, and P. Sánchez.

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Liaskos, S., Khan, S.M. & Mylopoulos, J. Modeling and reasoning about uncertainty in goal models: a decision-theoretic approach. Softw Syst Model 21, 1–24 (2022). https://doi.org/10.1007/s10270-021-00968-w

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