Enhancing Domain Knowledge for Requirements Elicitation with Web Mining

Kaiya, Haruhiko; Shimizu, Yuka; Hirotaka, Yasui; Kaijiri, Kenji; Saeki, Motoshi

doi:10.1109/apsec.2010.11

Cited by 19 publications

(10 citation statements)

References 20 publications

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“…As discussed above and shown in Table , there are considerable number of studies related to knowledge and KM in REP. In fact, although significant research exists regarding different aspects of KM such as knowledge sharing (Maio, ), knowledge creation (Wan, Zhang, Wan, & Huang, ), knowledge conversion (Wan et al, ), knowledge transfer (Shan et al, ), and domain knowledge (Kaiya, Shimizu, Yasui, Kaijiri, & Saeki, ; Niknafs & Berry, ) in requirement engineering, REP, software development, and system engineering, little is known about practical approaches for knowledge assessment and auditing in REP (Wai, Ammuthavali, & Marini, ). There is also a lack of knowledge assessment in REP in the previous research literature.…”

Section: Existing Studiesmentioning

confidence: 99%

“…As discussed above and shown in Table 2 (Kaiya, Shimizu, Yasui, Kaijiri, & Saeki, 2010;Niknafs & Berry, 2012) in requirement engineering, REP, software development, and system engineering, little is known about practical approaches for knowledge assessment and auditing in REP (Wai, Ammuthavali, & Marini, 2014).…”

Section: Existing Knowledge Studies In Requirements Elicitation Promentioning

confidence: 99%

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A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation

Taheri

Shafazand

et al. 2017

Knowl Process Manag

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This paper aims to develop a knowledge audit (KA) model with the focus on knowledge assessment in the requirements elicitation process (REP) to allay the problems of REP regarding knowledge communication. The principal problems with REP are knowledge conflict and the failure to mention a variety of knowledge and requirements changes. Despite of many existing studies relating to KA, inadequate effort has been directed towards investigating the full part played by the KA process in REP. The purpose of this paper is to bridge this gap using a software prototype that uses the KA model in the REP. This study proposes a KA model using an iterative triangulation method. The proposed model is validated through a case study by using a software prototype developed based on the proposed KA model to see if this KA model is effective for software developers in REP by improving the completeness, correctness, and understandability of the elicited requirements knowledge. Research findings are based on responses of 40 respondents from software development organizations. The results of case study confirmed the effectiveness of KA model for REP with respect to completeness, correctness, and understandability. This research answers the call to assess knowledge in REP by developing a KA model and prototype to fill the existing gap in this area. Overall, a KA model for REP is introduced and validated to identify and assess knowledge that supports knowledge communication in REP.

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Section: Existing Studiesmentioning

confidence: 99%

Section: Existing Knowledge Studies In Requirements Elicitation Promentioning

confidence: 99%

A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation

Taheri

Shafazand

et al. 2017

Knowl Process Manag

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“…These libraries are actively contributed to by many researchers and developers in the semantics web area. In addition, techniques for extracting ontology elements from natural language texts (Goldin and Berry, 1997), ontology learning (Maedche and Staab, 2001) and web-mining (Kaiya et al, 2010) can augment the ontology elements collection process.…”

Section: How To Build and Maintain An Ontology?mentioning

confidence: 99%

“…However, most existing research within this paradigm either lacks or insufficiently supports the creation and maintenance of domain knowledge and semantics of requirements. These play important roles in specifying system requirements (Zave and Jackson, 1997;Kenzi et al, 2010;Kaiya et al, 2010). Requirements can be viewed as optative statements that capture stakeholder demands, whose understanding requires domain knowledge to help bridge between a stakeholder's design on what a system needs to do and what is practically implementable in that system.…”

Section: Introductionmentioning

confidence: 99%

KBRE: a framework for knowledge-based requirements engineering

et al. 2013

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Detecting inconsistencies is a critical part of Requirements Engineering (RE) and has been a topic of interest for several decades. Domain knowledge and semantics of requirements not only play important roles in elaborating requirements but are also a crucial way to detect conflicts among them. In this paper we present a novel knowledge-based RE framework (KBRE) in which domain knowledge and semantics of requirements are central to elaboration, structuring, and management of captured requirements. Moreover, we also show how they facilitate the identification of requirements inconsistencies and other related problems. In our KBRE model, Description Logic (DL) is used as the fundamental logical system for requirements analysis and reasoning. In addition, the application of DL in the form of Manchester OWL Syntax brings simplicity to the formalization of requirements while preserving sufficient expressive power. A tool has been developed and applied to an industrial use case to validate our approach.

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“…A new requirements elicitation method was proposed by Kaiya and Saeki naming ORE (Ontology based Requirements Elicitation), where a domain ontology is used as domain knowledge to give meanings to requirements statements by using a semantic function [10]. In [11] the authors claimed that they boosted the completeness and correctness of requirements through a method for enhancing domain knowledge but they used web mining not KA. In [16] a knowledge management framework was developed based on the SECI model of knowledge creation, aiming to exploit tacit and explicit knowledge related to software requirements.…”

Section: Introductionmentioning

confidence: 99%

Identifying knowledge components in software requirement elicitation

Taheri

Abdullah

et al. 2014

2014 IEEE International Conference on Industrial Engineering and Engineering Management

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This paper considers the importance of knowledge in software development organizations which are highly knowledge-intensive and focuses on knowledge audit in their requirement elicitation process. Requirement elicitation process involves a great deal of knowledge and there are several problems regarding eliciting and using the knowledge in this process. Misunderstanding, undefined scope, conflicting information and constant changes of requirements are some of the problems of requirement elicitation. A knowledge audit model is proposed in this paper to improve the requirement elicitation process by identifying knowledge components and knowledge sources existing in the requirement elicitation process as well as their relationships. A survey is then conducted to prove the validity of the model. The results support that the proposed knowledge components and knowledge audit model improves requirement elicitation.

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Enhancing Domain Knowledge for Requirements Elicitation with Web Mining

Cited by 19 publications

References 20 publications

A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation

A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation

KBRE: a framework for knowledge-based requirements engineering

Identifying knowledge components in software requirement elicitation

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