A QOS Ontology-Based Component Selection

Yessad, Lamia; Boufaïda, Zizette

doi:10.5121/ijsc.2011.2302

Cited by 3 publications

(6 citation statements)

References 14 publications

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“…In this category, some papers have presented approaches based on various concepts and theories. For example, Yessad and Boufaida presented a method based on ontology with the following goals: describing the quality of service (QoS) of software components; selecting the relevant components for the developer's QoS requirements. …”

Section: Component Selectionmentioning

confidence: 99%

“…49 11,64-67,72-96 Nonfunctional requirements Include nonfunctional requirements 18 11,65,66,74,75,77-79,81,87,90-94,96,97 Provides a set of constraints deriving from how particular applications are built; this Software structure includes components and design patterns 10 11,65,67,79,81,83,93,95,97,98 used, communication and interface standards, and platform characteristics Abbreviation: NP, number of papers. Number of required interfaces 91,95 Number of components 11,64,72,73,76,84,86,88,92 Not mentioned 83 Delivery time 66,67,74,78,81,88 Testing time 81 Functionality 66,[77][78][79]85,90 Adaption time 97 Number of modules in the software 65,67,75,78,93 Training time 97 Number of provided interfaces 64,72,86,91,95 Deviational time 97 Number of alternative COTS available 65 Development time 66,81,83 11 Source lines of code 67,…”

Section: Criteria Used In Csmentioning

confidence: 99%

“…Analytic hierarchy process (AHP) 1 97 Weighted scoring method (WSM) 1 83 Clustering Fuzzy clustering 2 77,90 SIREN method 101 Customized Algorithms Ontology-based 1 91 Customized algorithm based on XML Query Language 1 94 Customized algorithm based on backtracking algorithm 1 95 Abbreviations: GA, genetic algorithm; NP, number of papers. consistency checked.…”

Section: Multiple-criteria Decision-making (Mcdm)mentioning

confidence: 99%

“…In 37,90 Case study details are mentioned in the papers ReMap 1 79 102 Mobile application 1 81 103 Hypertext Browser Selection 1 79 104 Mail server 1 82 105 Custom system 2 1 67 106 Garment Industry Financial System 1 11 Case study details are mentioned in the paper Pharmaceutical Industry Financial System 1 11 Case study details are mentioned in the paper Abbreviation: NP, number of papers. Comparison with suppliers/engineer opinion 80,97 Customize Fitness Function/Tools 75,80,[84][85][86][87] Wilcoxon statistical test 72 Comparison with other approaches 11,64,76,88,90 10-fold cross validation 98 Sensitivity analysis 11,89,93 Precision and recall 91 addition, for a large number of COTS alternatives and evaluation criteria, the decision-making process can be very complex, and numerous possible situations may arise from this complexity. The field of quality attribute determination of a component-based system is extensive; therefore, more research should be performed in this field.…”

Section: Future Workmentioning

confidence: 99%

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Software component identification and selection: A research review

Gholamshahi

Hasheminejad

2018

Softw Pract Exp

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Summary Nowadays, with the development of software reuse, software developers are paying more attention to component‐related technologies, which have been mostly applied in the development of large‐scale complex applications to enhance the productivity of software development and accelerate time to market. Component‐based software development is well acknowledged as a methodology, which establishes the reusability of software and reduces the development cost effectively. Two crucial problems in component‐based software development are component identification and component selection. The main purpose of this paper is to provide a reference point for future research by categorizing and classifying different component identification and component selection methods and emphasizing their respective strengths and weaknesses. We hope that it can help researchers find the current status of this issue and serve as a basis for future activities.

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Section: Component Selectionmentioning

confidence: 99%

Section: Criteria Used In Csmentioning

confidence: 99%

Section: Multiple-criteria Decision-making (Mcdm)mentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

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Software component identification and selection: A research review

Gholamshahi

Hasheminejad

2018

Softw Pract Exp

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“…More specifically, they addressed service gaps and overlaps extending the traditional compatibility and substitutability operators to deal with components that support multiple interfaces. Yessad and Boufaida (2011) proposed a Quality of Service (QoS) ontology for describing software components and used this ontology to semantically select relevant components based on the QoS specified by the developer. Pahl (2007) presented an approach for component matching by encoding transitional reasoning about safety and liveness properties into description logic and a Web standards compliant ontology framework.…”

Section: Literature Overviewmentioning

confidence: 99%

Towards a CBSE Framework for Enhancing Software Reuse: Matching Component Properties Using Semi-formal Specifications and Ontologies

Andreou

Papatheocharous²

2016

Communications in Computer and Information Science

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A novel Component-based Software Engineering (CBSE) framework is proposed in this work that focuses on enhancing the reuse process by offering support for locating appropriate components. The architecture of the framework comprises of five interrelated layers, namely Description, Location, Analysis, Recommendation and Build. The scope of this work is to describe in detail the first and third layers, and provide the means to evaluate the suitability of candidate software components for reuse. The overall aim is to facilitate components' profiling and offer efficient matching of system and software requirements to increase the reusability potential of components. A specifications profile is created for each component using a semi-formal natural language that describes certain properties. A dedicated parser recognizes parts of the profile and translates them into instance values of a dedicated CBSE ontology that encodes these properties. Matching is then performed at the level of ontology instances between the available components and the components required by the developer. The framework recommends components based on a suitability ratio that calculates their distances from the desired properties.

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Optimal components selection based on fuzzy-intra coupling density for component-based software systems under build-or-buy scheme

Kalantari

Motameni

Akbari

et al. 2021

Complex Intell. Syst.

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Component-Based Software Engineering (CBSE) is an approach to building and developing software systems based on software components. In component-based software systems, there are various software components, including Commercial off the Shelf (COTS) and in-house components. Software developers can build their desired software component as in-house or COTS. The problem of deciding optimally between COTS and in-house components is one of the major challenges of software developers, which is known as the component selection problem. This can be resolved by evaluating the criteria for optimality in component selection and then solving the component selection problem by optimization techniques. In this paper, an attempt was made to optimize the component selection problem through the multi-objective optimization by maximizing the Fuzzy-Intra Coupling Density (Fuzzy-ICD) and functionality as objective functions, and also taking into account budget, delivery time, reliability, and Fuzzy-ICD as constraints of multi-objective problems. Fuzzy ICD is a more accurate criterion to calculate the relationship between Cohesion and Coupling of components, which is obtained through the fuzzy computing of each of them, based on the Meyers classification. Thus, after a two-criterion optimization model formulation, this optimization problem was solved by fuzzy multi objectives approach. Finally, the proposed method was evaluated by performing the case study of financial-accounting system. Comparison of the results showed that the proposed method could select optimal components with maximum functionality and Fuzzy-ICD and fewer rates of time and Budget (0.29, 0.43, 1.1 s, and 88$ were the improved rates of functionality, Fuzzy-ICD, time, and budget, respectively).

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A QOS Ontology-Based Component Selection

Abstract: ABSTRACT

Cited by 3 publications

References 14 publications

Software component identification and selection: A research review

Software component identification and selection: A research review

Towards a CBSE Framework for Enhancing Software Reuse: Matching Component Properties Using Semi-formal Specifications and Ontologies

Optimal components selection based on fuzzy-intra coupling density for component-based software systems under build-or-buy scheme

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