A Model for Enhancing Tacit Knowledge Flow in Non-functional Requirements Elicitation

Buitrn, Sandra L.; Pino, Francisco J.; Flores-Ríos, Brenda L.; Ibarra-Esquer, Jorge E.; Astorga-Vargas, María Angélica

doi:10.1109/conisoft.2017.00011

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…The ontology in ElicitO implements ISO9126 [57]. The MERliNN framework suggests procedures to identify and manage knowledge flows in the elicitation and analysis process [21]. One paper evaluating a tool for QUARCC and S-Cost-knowledge-based tools for handling inter-relationships among quality requirements and stakeholders [55].…”

Section: Knowledge Managementmentioning

confidence: 99%

A systematic literature review of empirical research on quality requirements

2022

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Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements.

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Section: Knowledge Managementmentioning

confidence: 99%

A systematic literature review of empirical research on quality requirements

2022

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“…In [45]researchers suggested a step wise approach using the ISO 9126 quality characteristics as a guideline for the requirement elicitor to capture social, organizational and human factors that can improve overall quality of software product. In [46] researchers suggested the analytic network process, based on multi-criteria decision making for requirement elicitation technique selection.…”

Section: Related Workmentioning

confidence: 99%

Selection of Requirement Elicitation Techniques: A Neural Network based Approach

Muqeem¹,

Ahmad²,

Nazeer³

et al. 2022

IJACSA

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Requirement Elicitation is key activity of requirement engineering and has a strong impact on design and other phases of software development life cycle. Poor requirement engineering practices lead to project failure. A sound requirement elicitation process is the foundation for the overall quality of software product. Due to criticality and high impact of this phase on overall success and failure of projects, it is very necessary to perform the requirements elicitation activities in a perfect and specific manner. The most difficult and demanding jobs during Requirement Elicitation phase is to select appropriate and specific technique from a wide array of techniques and tools. In this paper, a new approach is proposed using an artificial neural network for selection of requirement elicitation technique from a wide variety of tools and techniques that are available. The training of Neural Network is done by back propagation algorithm. The trained and resultant network can be used as a base for selection of requirement elicitation techniques.

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“…Essa ferramenta propõe o uso de ontologias e técnicas de Web Semântica para facilitar o armazenamento e a recuperação de conhecimentos.  Buitron et al (Buitron et al, 2017)…”

Section: Reutilização De Conhecimento Em Rnfsunclassified

Construção Colaborativa De Requisitos De Qualidade

LINHARES¹

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A Model for Enhancing Tacit Knowledge Flow in Non-functional Requirements Elicitation

Cited by 6 publications

References 33 publications

A systematic literature review of empirical research on quality requirements

A systematic literature review of empirical research on quality requirements

Selection of Requirement Elicitation Techniques: A Neural Network based Approach

Construção Colaborativa De Requisitos De Qualidade

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