A Vision Towards A Conceptual Basis for the Systematic Treatment of Uncertainty in Goal Modelling

Alwidian, Sanaa; Dhaouadi, Mouna; Famelis, Michalis

doi:10.1145/3419804.3421451

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…To support the decision-making process in analysis and design phases, early in the requirement engineering process, Horkoff et al [97] presented a formal iterative goal analysis process with a tool that integrated i* goal models with the MAVO framework [99] to remove unnecessary requirements alternatives. The treatment of uncertainty in general goal modeling is further explored by Alwidian et al [105]. Language Usage Self-adaptive systems offer an opportunity for more relaxed language to be used to better specify their requirements, because common patterns such as "the system shall do this" are often too strict that context.…”

Section: Assessment Resultsmentioning

confidence: 99%

Self-Adaptation Driven by SysML and Goal Models -- A Literature Review

Anda¹,

Amyot²

2022

EISEJ

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Background: Socio-cyber-physical systems (SCPSs) are a type of cyber-physical systems with social concerns. Many SCPSs, such as smart homes, must be able to adapt to reach an optimal symbiosis with users and their contexts. The Systems Modeling Language (SysML) is frequently used to specify ordinary CPSs, whereas goal modeling is a requirements engineering approach used to describe and reason about social concerns. Objective: This paper aims to assess existing modeling techniques that support adaptation in SCPSs, and in particular those that integrate SysML with goal modeling. Method: A systematic literature review presents the main contributions of 52 English articles selected from five databases that use both SysML and goal models (17 techniques), SysML models only (11 techniques), or goal models only (8 techniques) for analysis and self-adaptation. Result: Existing techniques have provided increasingly better modeling support for adaptation in a SCPS context, but overall analysis support remains weak. The techniques that combine SysML and goal modeling offer interesting benefits by tracing goals to SysML (requirements) diagrams and influencing the generation of predefined adaptation strategies for expected contexts, but few target adaptation explicitly and most still suffer from a partial coverage of important goal modeling concepts and of traceability management issues.

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Section: Assessment Resultsmentioning

confidence: 99%

Self-Adaptation Driven by SysML and Goal Models -- A Literature Review

Anda¹,

Amyot²

2022

EISEJ

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Uncertainty-Based Metamorphic Testing for Validating Plagiarism Detection Systems

Chan,

Keung,

Yang

2023

Communications in Computer and Information Science

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Reasoning about Confidence in Goal Satisfaction

2022

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Goal models are commonly used requirements engineering artefacts that capture stakeholder requirements and their inter-relationships in a way that supports reasoning about their satisfaction, trade-off analysis, and decision making. However, when there is uncertainty in the data used as evidence to evaluate goal models, it is crucial to understand the confidence or trust level in such evaluations, as uncertainty may increase the risk of making premature or incorrect decisions. Different approaches have been proposed to tackle goal model uncertainty issues and risks. However, none of them considers simple quality measures of collected data as a starting point. In this paper, we propose a Data Quality Tagging and Propagation Mechanism to compute the confidence level of a goal’s satisfaction level based on the quality of input data sources. The paper uses the Goal-oriented Requirement Language (GRL), part of the User Requirements Notation (URN) standard, in examples, with an implementation of the proposed mechanism and a case study conducted in order to demonstrate and assess the approach. The availability of computed confidence levels as an additional piece of information enables decision makers to (i) modulate the satisfaction information returned by goal models and (ii) make better-informed decisions, including looking for higher-quality data when needed.

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A Vision Towards A Conceptual Basis for the Systematic Treatment of Uncertainty in Goal Modelling

Cited by 3 publications

References 15 publications

Self-Adaptation Driven by SysML and Goal Models -- A Literature Review

Self-Adaptation Driven by SysML and Goal Models -- A Literature Review

Uncertainty-Based Metamorphic Testing for Validating Plagiarism Detection Systems

Reasoning about Confidence in Goal Satisfaction

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