PRISE: A process to support iStar extensions

Gonçalves, Elaine Cristine Piffer; Araújo, João; Castro, Jaelson

doi:10.1016/j.jss.2020.110649

Cited by 13 publications

(11 citation statements)

References 91 publications

(213 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our hypothesis is that by teaching DS with a goal-oriented perspective, students will be able to experience a better understanding of the main goals of every DS, what their qualities are, and relationships with their context of use. To test this hypothesis, we follow the PRISE method [15] to extend iStar2.0 with constructs that facilitate teaching and learning DS with respect to functional and non-functional requirements. In this paper we present iStarDust: iStar 2.0 for DS, and report on an initial validation regarding students' effectiveness by means of a controlled experiment.…”

Section: Discussionmentioning

confidence: 99%

“…The reason is that iStar2.0 has been proposed as a standard di facto in the i* community with the purpose of having an agreed definition of the language core constructs. • We follow the PRISE method [15] to extend iStar2.0 with constructs that fit the goal.…”

Section: Problem Scopementioning

confidence: 99%

“…From this goal and these decisions, we derive the following research questions: RQ1: What extensions can be applied over iStar 2.0 to make it suitable to describe DS? Following PRISE [15], we will identify the constructs needed to make iStar2.0 suitable to the DS domain. We will identify the needs, conceptualize the solution, and extend the iStar2.0 metamodel [6].…”

Section: Problem Scopementioning

confidence: 99%

See 2 more Smart Citations

Goal-Oriented Models for Teaching and Understanding Data Structures

Franch

Ruiz

2021

Conceptual Modeling

View full text Add to dashboard Cite

Most computer science curricula include a compulsory course on data structures. Students are prone to memorise facts about data structures instead of understanding the essence of underlying concepts. This can be explained by the fact that learning the basics of each data structure, the difference with each other, and the adequacy of each of them to the most appropriate context of use, is far from trivial. This paper explores the idea of providing adequate levels of abstractions to describe data structures from an intentional point of view. Our hypothesis is that adopting a goal-oriented perspective could emphasise the main goals of each data structure, its qualities, and its relationships with the potential context of use. Following this hypothesis, in this paper we present the use of iStar2.0 to teach and understand data structures. We conducted a comparative quasi-experiment with undergraduate students to evaluate the effectiveness of the approach.Significant results show the great potential of goal modeling for teaching technical courses like data structures. We conclude this paper by reflecting on further teaching and conceptual modeling research to be conducted in this field.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Problem Scopementioning

confidence: 99%

Section: Problem Scopementioning

confidence: 99%

See 1 more Smart Citation

Goal-Oriented Models for Teaching and Understanding Data Structures

Franch

Ruiz

2021

Conceptual Modeling

View full text Add to dashboard Cite

show abstract

“…A catalogue of extensions can help reuse and avoid symbol redundancy and overload [11]. Also, the adapted method is better used as part of a systematic process for extensions creation [34,35]. Also, it is possible to apply the adapted method to propose new symbols of extensions of other modelling languages.…”

Section: Discussionmentioning

confidence: 99%

Using empirical studies to mitigate symbol overload in iStar extensions

et al. 2019

Self Cite

View full text Add to dashboard Cite

Modelling languages are frequently extended to include new constructs to be used together with the original syntax. New constructs may be proposed by adding textual information, such as UML stereotypes, or by creating new graphical representations. Thus, these new symbols need to be expressive and proposed in a careful way to increase the extension's adoption. A method to create symbols for the original constructs of a modelling language was proposed and has been used to create the symbols when a new modelling language is designed. We argue this method can be used to recommend new symbols for the extension's constructs. However, it is necessary to make some adjustments since the new symbols will be used with the existing constructs of the modelling language original syntax. In this paper, we analyse the usage of this adapted method to propose symbols to mitigate the occurrence of overloaded symbols in the existing iStar extensions. We analysed the existing iStar extensions in an SLR and identified the occurrence of symbol overload among the existing constructs. We identified a set of fifteen overloaded symbols in existing iStar extensions. We used these concepts with symbol overload in a multi-stage experiment that involved users in the visual notation design process. The study involved 262 participants, and its results revealed that most of the new graphical representations were better than those proposed by the extensions, with regard to semantic transparency. Thus, the new representations can be used to mitigate this kind of conflict in iStar extensions. Our results suggest that next extension efforts should consider user-generated notation design techniques in order to increase the semantic transparency.

show abstract

“…This work is part of a set of studies to analyze and systematize the KAOS extensions similarly as performed by Gonçalves et al [3] with iStar extensions This paper is structured as follows: Section 2 presents the background about modelling languages and their extensions, KAOS and catalogues. Section 3 presents related works.…”

Section: Introductionmentioning

confidence: 99%

A Catalogue of KAOS Extensions

Carvalho Junior,

Gonçalves,

Carvalho

et al. 2022

Anais Do Workshop Em Engenharia De Requisitos

View full text Add to dashboard Cite

KAOS, a goal-based modeling language, has been extended since its creation. Searching for existing KAOS extensions and their constructs is a task that can be the start point for requirements modeling or for the creation of new KAOS extensions. This search can be performed by using strings searches in databases or through a catalogue that supports them. This exploratory task about extensions can take a great deal of time and be prone to failure when performed without specific and adequate support. catalogues have been used successfully to bring together a body of knowledge, including knowledge of modeling language extensions. Motivated by this scenario, this work proposes a catalogue of extensions to the KAOS modeling language. The results suggest that the proposed catalogue can be used to retrieve information about extensions and their constructs correctly and that it is easy to use.

show abstract

PRISE: A process to support iStar extensions

Cited by 13 publications

References 91 publications

Goal-Oriented Models for Teaching and Understanding Data Structures

Goal-Oriented Models for Teaching and Understanding Data Structures

Using empirical studies to mitigate symbol overload in iStar extensions

A Catalogue of KAOS Extensions

Contact Info

Product

Resources

About