Combining agent-oriented conceptual modelling with formal methods

Vilkomir, Sergiy; Ghose, Aditya; Krishna, Aneesh

doi:10.1109/aswec.2004.1290467

Cited by 13 publications

(10 citation statements)

References 11 publications

(12 reference statements)

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“…This paper is an extension and refinement of our earlier results [24,17,18] and is structured as follows. In Section 2, we present formal basis for co-evolution of models.…”

Section: Introductionmentioning

confidence: 94%

Consistency preserving co-evolution of formal specifications and agent-oriented conceptual models

Krishna

Vilkomir

Ghose

2009

Information and Software Technology

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“…This paper is an extension and refinement of our earlier results [24,17,18] and is structured as follows. In Section 2, we present formal basis for co-evolution of models.…”

Section: Introductionmentioning

confidence: 94%

Consistency preserving co-evolution of formal specifications and agent-oriented conceptual models

Krishna

Vilkomir

Ghose

2009

Information and Software Technology

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“…optimization). After that, i* framework has been combined with other software engineering techniques [35,36] . The recent work is done by Castro et al [37,38] in Tropos methodology, which divides the software development process into early requirement analysis (i* framework), late requirement analysis, system architecture, and detail design, which can be carried out by Jack programming platform.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

From knowledge based software engineering to knowware based software engineering

Jin

2008

Sci. China Ser. F-Inf. Sci.

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The first part of this paper reviews our efforts on knowledge-based software engineering, namely PROMIS, started from 1990s. The key point of PROMIS is to generate applications automatically based on domain knowledge as well as software knowledge. That is featured by separating the development of domain knowledge from the development of software. But in PROMIS, we did not find an appropriate representation for the domain knowledge. Fortunately, in our recent work, we found such a carrier for knowledge modules, i.e. knowware. Knowware is a commercialized form of domain knowledge. This paper briefly introduces the basic definitions of knowware, knowledge middleware and knowware engineering. Three life circle models of knowware engineering and the design of corresponding knowware implementations are given. Finally we discuss application system automatic generation and domain knowledge modeling on the J2EE platform, which combines the techniques of PROMIS, knowware and J2EE, and the development and deployment framework, i.e. PROMIS/KW**.PROMIS, knowledge based software engineering, knowware, J2EE, PROMIS/KW**, knowware based software engineering Generating applications automatically based on domain modeling and pseudo natural language understandingThe practice of software engineering tells us that, most failures of software development rely on the bad requirement analysis, and the reason for that falls upon lack of good cooperation between users and software engineers. Users, usually being unable to exactly and clearly give their requirements, want to keep the right of freely changing the requirements during the process of software developing so that it becomes difficult for software engineers to make a proper requirement analysis and to guarantee the accomplishment of the development job successfully.

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“…A set of mapping rules is defined to help ensure consistency between the two models. This paper is an extension and refinement of our earlier results [24,17,18] and is structured as follows. In Section 2, we present formal basis for co-evolution of models.…”

Section: Introductionmentioning

confidence: 99%

Requirements Engineering

2012

Reliable Design of Medical Devices, Third Edition

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a b s t r a c tMany modelling techniques tend to address ''late-phase" requirements while many critical modelling decisions (such as determining the main goals of the system, how the stakeholders depend on each other, and what alternatives exist) are taken during early-phase requirements engineering. The i * modelling framework is a semiformal agent-oriented conceptual modelling language that is well-suited for answering these questions. This paper addresses key challenge faced in the practical deployment of agent-oriented conceptual modelling frameworks such as i * . Our approach to addressing this problem is based on the observation that the value of conceptual modelling in the i * framework lies in its use as a notation complementary to existing requirements modelling and specification languages, i.e., the expressive power of i * complements rather than supplants that of existing notations. The use of i * in this fashion requires that we define methodologies that support the co-evolution of i * models with more traditional specifications. This research examines how this might be done with formal specification notations (specifically Z).

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Combining agent-oriented conceptual modelling with formal methods

Cited by 13 publications

References 11 publications

Consistency preserving co-evolution of formal specifications and agent-oriented conceptual models

Consistency preserving co-evolution of formal specifications and agent-oriented conceptual models

From knowledge based software engineering to knowware based software engineering

Requirements Engineering

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