A Method for the Definition of Metrics over i* Models

Franch, Xavier

doi:10.1007/978-3-642-02144-2_19

Cited by 19 publications

(9 citation statements)

References 14 publications

(26 reference statements)

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“…The method shall provide verifiable means to check that the model being generated fulfils some identified conditions about their structure. The use of metrics [20] could help here.…”

Section: Challenge 2: Providing Methodologies For I* Modellingmentioning

confidence: 99%

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Fostering the Adoption of i * by Practitioners: Some Challenges and Research Directions

Franch

2010

Intentional Perspectives on Information Systems Engineering

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The i* framework is a widespread formalism in the software engineering discipline that allows expressing intentionality of system actors. From the time it was issued, in the mid-nineties, a growing research community has adopted it either in its standard form or formulating variations in order to adapt it to some particular purpose. New methods, techniques and tools have made evolve the framework in a way that it may be currently considered quite mature from the scientific perspective. However, the i* framework has not been transferred to practitioners at the same extent yet: industrial experiences using i* are not many and have been mainly conducted by i* experts that are part of that very research community. Therefore, it may be argued that some steps are needed for boosting the adoption of i* by practitioners. In this chapter, we identify some scientific challenges whose overcoming could represent a step towards this goal. For each challenge, we present the problem that is addressed, its current state of the art and some envisaged lines of research.

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“…The method shall provide verifiable means to check that the model being generated fulfils some identified conditions about their structure. The use of metrics [20] could help here.…”

Section: Challenge 2: Providing Methodologies For I* Modellingmentioning

confidence: 99%

“…how well-suited it is for generating classes and attributes in a class model. The definition and application of metrics using the iMDF M method [20] is a possible path to follow.…”

Section: Challenge 4: Use I* Models In Later Development Phasesmentioning

confidence: 99%

Fostering the Adoption of i * by Practitioners: Some Challenges and Research Directions

Franch

2010

Intentional Perspectives on Information Systems Engineering

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“…They use a catalogue of patterns for defining metrics. In a related work, Franch proposes a method to better guide the analyst throughout the metrics definition process, over i* models [15]. The method is applied to evaluate business process performance.…”

Section: Related Workmentioning

confidence: 99%

Experimental Evaluation of Conceptual Modelling through Mind Maps and Model Driven Engineering

Wanderley

Silveira

Araújo

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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Abstract. Recent research studies report evidence that many systems' requirements are not fully understood, being difficult to elicit and produce accurate conceptual models more efficiently. We have been investigating how creative models and model-driven development can contribute to overcome this difficulty. Creative models such as mind maps offer effective cognitive support to rapidly produce conceptual models that are closer to stakeholders' expectations. This paper reports on the results of a controlled experiment carried out in academia and industry using a creative and agile modeling approach. This approach uses mind maps to generate conceptual models through transformations, using model-driven engineering techniques. The empirical evidence discussed in this paper shows a significant gain in time spent to build conceptual models.

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“…This framework uses a catalogue of patterns for defining metrics, and OCL to formulate these metrics. In a follow up work, Franch proposes a generic method to better guide the analyst throughout the metrics definition process, over i* models [24]. The method is applied to evaluate business process performance.…”

Section: Related Workmentioning

confidence: 99%

Identifying Modularity Improvement Opportunities in Goal-Oriented Requirements Models

Gralha

Goulão

Araújo

2014

Advanced Information Systems Engineering

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Abstract. Goal-oriented Requirements Engineering approaches have become popular in the Requirements Engineering community as they provide expressive model elements for requirements elicitation and analysis. However, as a common challenge, they are still struggling when it comes to managing the accidental complexity of their models. In this paper, we provide a set of metrics, which are formally specified and have tool support, to measure and analyze the complexity of goal models, in particular i* models. The aim is to identify refactoring opportunities to improve the modularity of those models, and consequently reduce their complexity. We evaluate these metrics by applying them to a set of well-known case studies from industry and academia. Our results allow the identification of refactoring opportunities in the evaluated models.

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A Method for the Definition of Metrics over i* Models

Cited by 19 publications

References 14 publications

Fostering the Adoption of i * by Practitioners: Some Challenges and Research Directions

Fostering the Adoption of i * by Practitioners: Some Challenges and Research Directions

Experimental Evaluation of Conceptual Modelling through Mind Maps and Model Driven Engineering

Identifying Modularity Improvement Opportunities in Goal-Oriented Requirements Models

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