A simulation-based fuzzy multi-attribute decision making for prioritizing software requirements

Ejnioui, A.; Otero, Carlos E.; Otero, Luis Daniel

doi:10.1145/2380790.2380800

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…Next, we notice that according to the published studies, the techniques based on fuzzy logic provide accuracy (e.g. [35,71,83,96,97]), while data-driven techniques are efficient not only in use but also deal with large amount of data [43]. Furthermore, despite the extensive usage of RP methods such as the AHP, a number of evaluation studies [59,70] point out the limitations of these methods.…”

Section: Requirements Prioritization Approaches (Rq1)mentioning

confidence: 99%

A systematic literature review on requirement prioritization techniques and their empirical evaluation

Bukhsh

Daneva

2020

Computer Standards & Interfaces

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Section: Requirements Prioritization Approaches (Rq1)mentioning

confidence: 99%

A systematic literature review on requirement prioritization techniques and their empirical evaluation

Bukhsh

Daneva

2020

Computer Standards & Interfaces

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“…Foi demonstrado que apesar da ocorrência de erros na priorização das atividades, os resultados obtidos encorajam a aplicação deste método na priorização de atividades de desenvolvimento de software. Não se pode afirmar que o método consegue atender as demandas de priorização de atividades de qualquer empresa de desenvolvimento de software, porém é uma abordagem promissora de priorização, já que não sofre com problemas de escalabilidade, tal como outros métodos de priorização existentes na literatura [10] e [27]. Além disso o método consegue priorizar rapidamente um conjunto de atividades, podendo ser executado a cada inserção de uma nova atividade na lista de espera para o desenvolvimento, mantendo assim a lista de atividades priorizada.…”

Section: Conclusãounclassified

“…Pode-se destacar também que grande parte dos métodos de PRS existentes não foram amplamente adotados, além de pesquisas apontarem que a maioria dos métodos sofrem com problemas de usabilidade e escalabilidade [10,27], o que justifica a pesquisa e desenvolvimento e validação de novos métodos de priorização.…”

Section: Introductionunclassified

Priorização de Atividades de Desenvolvimento de Software com uma Rede Neural Artificial

Silveira¹,

Marasca²,

Marcon³

et al. 2020

Anais Do XI Computer on the Beach - COTB '20

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Prioritization of development activities in software companies iscommonly performed arbitrarily and subjectively, based solely onthe experience of the company’s employees. Aiming to contributeto the automation of this process, this paper presents a methodof automatic prioritization of software development activities, usingan algorithm that combines the use of a numerical regressionbasedArtificial Neural Network (RNA) with an ordering method.To validate the accuracy of the proposed method, a case study wasconducted, with real data from a software development company.The experiments allowed to evaluate the accuracy of the proposedmethod qualitatively and quantitatively, generating satisfactoryresults, which indicate the feasibility of implementing the proposedmethod in the target company of the case study.

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“…In order to bridge the gap between theory and field implementation, it may be necessary to re-design the software interface prototype developed in MATLAB (see Figure 7) to facilitate the implementation of the proposed solution approach for practical purposes. Critical to this step will be a robust requirements engineering approach to ensure that critical functional needs are incorporated into the proposed solution [48], [49].…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Fuzzy Inference System Framework to Prioritize the Deployment of Resources in Low Visibility Traffic Conditions

Ortega

Otero

2019

IEEE Access

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Transportation managers and engineers are often required to make decisions regarding the use of limited resources that directly affect public safety, costs, and the overall performance of transportation systems. One important decision involves prioritizing the deployment of resources (e.g., personnel and variable signs) to low visibility areas due to fog or other environmental and road conditions. Due to the lack of proper approaches to characterize and incorporate weather and road condition parameters in the decisionmaking process, transportation managers depend mainly on personal experience to make these types of decisions. To help them prioritize the deployment of resources to low visibility areas, this research presents a fuzzy inference system (FIS) framework composed of three fuzzy systems that characterize fog occurrence, road risk conditions, and deployment of resources. Preliminary experiments to evaluate the developed fog occurrence FIS against four methods presented in the literature using data from two weather stations showed that the FIS model outperformed three of the other methods in accuracy. These results are very promising given that the other methods represent more expensive solution approaches that require large amounts of data, significant time-consuming data preparation, network architecture design tasks, and high processing power.

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A simulation-based fuzzy multi-attribute decision making for prioritizing software requirements

Cited by 16 publications

References 21 publications

A systematic literature review on requirement prioritization techniques and their empirical evaluation

A systematic literature review on requirement prioritization techniques and their empirical evaluation

Priorização de Atividades de Desenvolvimento de Software com uma Rede Neural Artificial

Fuzzy Inference System Framework to Prioritize the Deployment of Resources in Low Visibility Traffic Conditions

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