Analyzing goal models

Horkoff, Jennifer; Yu, Eric

doi:10.1145/1982185.1982334

Cited by 66 publications

(9 citation statements)

References 41 publications

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“…This process involves the iterative application of propagation rules to attach current values from each offspring to its parent and then resolving softgoal labels at the parent level [25]. We apply the rules for satisfaction analysis in goal models that are explained in [26] in the Evaluation phase.…”

Section: Evaluation Phasementioning

confidence: 99%

A Modeling Approach for Getting to Win-Win in Industrial Collaboration under Strategic Coopetition

Pant

2019

CSIMQ

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Interorganizational coopetition describes a relationship in which two or more organizations cooperate and compete simultaneously. Actors under coopetition cooperate to achieve collective objectives and compete to maximize their individual benefits. Such relationships are based on the logic of win-win strategies that necessitate decision-makers in coopeting organizations to develop relationships that yield favorable outcomes for each actor. We follow a strategic modeling approach that combines i* goal-modeling to explore strategic alternatives of actors with Game Tree decision-modeling to evaluate the actions and payoffs of those players. In this article, we elaborate on the method, illustrating one particular pathway towards a positive-sum outcomethrough the introduction of an intermediary actor. This article demonstrates the activation of one component in this guided approach of systematically searching for alternatives to generate a new win-win strategy. We also present a metamodel for relating i* models and Game Trees. A hypothetical industrial scenario focusing on the Industrial Data Space, which is a platform that can help organizations to overcome obstacles to data sharing in a coopetitive ecosystem, is used to explain this approach.

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Section: Evaluation Phasementioning

confidence: 99%

A Modeling Approach for Getting to Win-Win in Industrial Collaboration under Strategic Coopetition

Pant

2019

CSIMQ

Self Cite

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“…Much attention has been paid to the area of goal-oriented Requirements Engineering (RE) [2], [8] in the last two decades, and several studies that compare goal-oriented languages have been published. According to Siau and Rossi [23], these studies can be classified in three groups: feature comparison [19], [24], [25], [26], theoretical and conceptual evaluations [27], [28], [29] and empirical studies [12], [30], [31], [32], [33].…”

Section: Existing Studies Comparing Goal-oriented Languagesmentioning

confidence: 99%

“…Horkoff and Yu [25] surveyed available approaches for goal-oriented modeling (e.g., i*, GRL, Tropos, NFR, KAOS) and classified them according to several criteria (e.g., satisfaction analysis, metrics, planning, simulation). They also proposed guidelines that would assist in the use of these approaches, grouped into domain understanding, communication, model improvement, scoping, requirement elicitation, requirements improvement, and design, but the proposed guidelines need to be validated in practice.…”

Section: Regev and Wegmannmentioning

confidence: 99%

Assessing the effectiveness of goal-oriented modeling languages: A family of experiments

Abrahão

Insfrán

González-Ladrón-de-Guevara

et al. 2019

Information and Software Technology

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Context: Several goal-oriented languages focus on modeling stakeholders' objectives, interests or wishes. However, these languages can be used for various purposes (e.g., exploring system solutions or evaluating alternatives), and there are few guidelines on how to use these models downstream to the software requirements and design artifacts. Moreover, little attention has been paid to the empirical evaluation of this kind of languages. In a previous work, we proposed value@GRL as a specialization of the Goal Requirements Language (GRL) to specify stakeholders' goals when dealing with early requirements in the context of incremental software development.Objective: This paper compares the value@GRL language with the i* language, with respect to the quality of goal models, the participants' modeling time and productivity when creating the models, and their perceptions regarding ease of use and usefulness.Method: A family of experiments was carried out with 184 students and practitioners in which the participants were asked to specify a goal model using each of the languages. The participants also filled in a questionnaire that allowed us to assess their perceptions. Results:The results of the individual experiments and the meta-analysis indicate that the quality of goal models obtained with value@GRL is higher than that of i*, but that the participants required less time to create the goal models when using i*. The results also show that the participants perceived value@GRL to be easier to use and more useful than i* in at least two experiments of the family.Conclusions: value@GRL makes it possible to obtain goal models with good quality when compared to i*, which is one of the most frequently used goal-oriented modeling languages. It can, therefore, be considered as a promising emerging approach in this area. Several insights emerged from the study and opportunities for improving both languages are outlined.

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“…A hypothesis raised by the authors is that modeling the requirements described by initiatives associated with the I4.0 like the RAMI 4.0 (Reference Architectural Model for Industry 4.0) [6] using the KAOS (Keep All Objectives Satisfied) [7] methodology can assist in developing systems that effectively reflect the needs of the industry.…”

Section: Introductionmentioning

confidence: 99%

Big Data on Machine to Machine Integration’s Requirement Analysis Within Industry 4.0

Coda

Salles

Vitoi

et al. 2019

IFIP Advances in Information and Communication Technology

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One of the foundations for Industry 4.0 is the integration of various industrial elements (i.e. sensors, machines, and services) so that these devices can decide in a relatively autonomous way the level of integration which will be adopted. Thus, it is important to understand how the communication Machine to Machine is effectively realized and how these data can be explored and used to enhance the manufacturing process. The exchange of information between machines in the industrial process represents a potential to acquire and analyze a mass of data characterized as "big data", which can be perceived as an opportunity to discuss the paradigms of the industrial systems. Therefore, the purpose of this research is to identify the requirements for the Machine to Machine communication and the use of this data/information for more complexes analyzes using big data and analytics techniques. The KAOS methodology was utilized to model these requirements.

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Analyzing goal models

Cited by 66 publications

References 41 publications

A Modeling Approach for Getting to Win-Win in Industrial Collaboration under Strategic Coopetition

A Modeling Approach for Getting to Win-Win in Industrial Collaboration under Strategic Coopetition

Assessing the effectiveness of goal-oriented modeling languages: A family of experiments

Big Data on Machine to Machine Integration’s Requirement Analysis Within Industry 4.0

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