219,129 research outputs found

    Using contextual goal models for constructing situational methods

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    Situation and intention are two fundamental notions in situational method engineering (SME). They are used to assess the context of an ISD project and to specify method requirements in this context. They also allow defining the goals of the method chunks and the conditions under which they can be applied. In this way, the selection and assembly of method chunks for a particular ISD project is driven by matching situational method requirements to method chunks’ goals and context descriptions. In this paper we propose the use of contextual goal models for supporting all SME steps. Our approach is based on iStar2.0 modeling language that we extend with contextual annotations.Peer ReviewedPostprint (author's final draft

    A situational approach for the definition and tailoring of a data-driven software evolution method

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    Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.Peer ReviewedPostprint (author's final draft

    Enhancing the Guidance of the Intentional Model "MAP": Graph Theory Application

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    The MAP model was introduced in information system engineering in order to model processes on a flexible way. The intentional level of this model helps an engineer to execute a process with a strong relationship to the situation of the project at hand. In the literature, attempts for having a practical use of maps are not numerous. Our aim is to enhance the guidance mechanisms of the process execution by reusing graph algorithms. After clarifying the existing relationship between graphs and maps, we improve the MAP model by adding qualitative criteria. We then offer a way to express maps with graphs and propose to use Graph theory algorithms to offer an automatic guidance of the map. We illustrate our proposal by an example and discuss its limitations.Comment: 9 page

    A Requirement-centric Approach to Web Service Modeling, Discovery, and Selection

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    Service-Oriented Computing (SOC) has gained considerable popularity for implementing Service-Based Applications (SBAs) in a flexible\ud and effective manner. The basic idea of SOC is to understand users'\ud requirements for SBAs first, and then discover and select relevant\ud services (i.e., that fit closely functional requirements) and offer\ud a high Quality of Service (QoS). Understanding usersÂ’ requirements\ud is already achieved by existing requirement engineering approaches\ud (e.g., TROPOS, KAOS, and MAP) which model SBAs in a requirement-driven\ud manner. However, discovering and selecting relevant and high QoS\ud services are still challenging tasks that require time and effort\ud due to the increasing number of available Web services. In this paper,\ud we propose a requirement-centric approach which allows: (i) modeling\ud usersÂ’ requirements for SBAs with the MAP formalism and specifying\ud required services using an Intentional Service Model (ISM); (ii)\ud discovering services by querying the Web service search engine Service-Finder\ud and using keywords extracted from the specifications provided by\ud the ISM; and(iii) selecting automatically relevant and high QoS services\ud by applying Formal Concept Analysis (FCA). We validate our approach\ud by performing experiments on an e-books application. The experimental\ud results show that our approach allows the selection of relevant and\ud high QoS services with a high accuracy (the average precision is\ud 89.41%) and efficiency (the average recall is 95.43%)
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