Domain knowledge reuse during requirements engineering

Gibson, Michael D.; Conheeney, Kevin

doi:10.1007/3-540-59498-1_253

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The precise capture, understanding, and representation of requirements is a crucial step in the development of effective and usable information systems Gibson and Conheeney (1995). Requirements are often error-prone due to misinterpretation of natural languages Fabbrini et al (2001).…”

Section: Requirementsmentioning

confidence: 99%

Software and Systems Traceability

Cleland‐Huang¹,

Gotel²,

Zisman³

2012

130

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Section: Requirementsmentioning

confidence: 99%

Software and Systems Traceability

Cleland‐Huang¹,

Gotel²,

Zisman³

2012

130

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“…Such a knowledge-based approach that enables instantiating a target system specification from the domain model asks for an application-domain independent prototype environment that provides "rules for generating target system specifications from the domain model" [16]. In order to reuse not only system requirements already formulated but the underlying knowledge acquired during requirement elicitation, modeling and validation activities, the common representation of domain knowledge is needed where "requirement knowledge allows exploitation by common tools" and "the developer is able to browse a populated domain knowledge model alongside the building and population of a new requirement model" [17]. These findings also support the efforts towards creating WBAH ontology that will enable requirements engineers reuse already acquired domain knowledge, which seems of great importance given the complexity of WBAH environments.…”

Section: A Knowledge-based Requirements Engineeringmentioning

confidence: 99%

“…In the following few decades significant research efforts have been made to address different knowledge-based engineering aspects to improve traditional RE. The role of domain knowledge representation in requirements elicitation has been addressed stating "not only the contents but also the representation is important" and defining and comparing various metrics to evaluate the effect of the representations on the quality of requirements specifications [17]. The framework for knowledge-based RE supporting automatic detection of a range of inconsistencies between requirements has been proposed [18].…”

Section: A Knowledge-based Requirements Engineeringmentioning

confidence: 99%

Towards Ontology-Based Requirements Engineering for IoT-Supported Well-Being, Aging and Health

Belani¹,

Šolić²,

Perković³

2022

2022 IEEE 30th International Requirements Engineering Conference Workshops (REW)

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Ontologies serve as a one of the formal means to represent and model knowledge in computer science, electrical engineering, system engineering and other related disciplines. Ontologies within requirements engineering may be used for formal representation of system requirements. In the Internet of Things, ontologies may be used to represent sensor knowledge and describe acquired data semantics. Designing an ontology comprehensive enough with an appropriate level of knowledge expressiveness, serving multiple purposes, from system requirements specifications to modeling knowledge based on data from IoT sensors, is one of the great challenges. This paper proposes an approach towards ontology-based requirements engineering for well-being, aging and health supported by the Internet of Things. Such an ontology design does not aim at creating a new ontology, but extending the appropriate one already existing, SAREF4EHAW, in order align with the well-being, aging and health concepts and structure the knowledge within the domain. Other contributions include a conceptual formulation for Well-Being, Aging and Health and a related taxonomy, as well as a concept of One Well-Being, Aging and Health. New attributes and relations have been proposed for the new ontology extension, along with the updated list of use cases and particular ontological requirements not covered by the original ontology. Future work envisions full specification of the new ontology extension, as well as structuring system requirements and sensor measurement parameters to follow description logic.

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“…The approach of deriving conceptual schema of information systems and databases from ontologies has not been explored in detail in the literature, yet we believe it may yield important benefits. Gibson and Conheeney (1995) suggest that requirements models should be “developed by specializing, refining or adapting selected parts of the relevant domain model, if this is appropriate”. Swartout et al (1996) use the SENSUS ontology to derive the conceptual schema for an air campaign planning system.…”

Section: Related Workmentioning

confidence: 99%

Maintaining web application: an ontology‐based reverse engineering approach

Benslimane¹,

Malki²,

Bouchiha³

2009

International Journal of Web Information Systems

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PurposeWeb applications are subject to continuous changes and rapid evolution triggered by increasing competition, especially in commercial domains such as electronic commerce. Unfortunately, usually they are implemented without producing any useful documentation for subsequent maintenance and evolution. Thereof, the maintenance of such systems becomes a challenging problem as the complexity of the web application grows. Reverse engineering has been heralded as one of the most promising technologies to support effective web application maintenance. This paper aims to present a reverse engineering approach that helps understanding existing undocumented web applications to be maintained or evolved.Design/methodology/approachThe proposed approach provides reverse engineering rules to generate a conceptual schema from a given domain ontology by using a set of transformation rules. The reverse engineering process consists of four phases: extracting useful information; identifying a set of ontological constructs representing the concepts of interest; enriching the identified set by additional constructs; and finally deriving a conceptual schema.FindingsThe advantage of using ontology for conceptual data modeling is the reusability of domain knowledge. As a result, the conceptual data model will be made faster, easier and with fewer errors than creating it in usual way. Designers can use the extracted conceptual schema to gain a better understanding of web applications and to assist in their maintenance.Originality/valueThe strong point of this approach is that it relies on a very rich semantic reference that is domain ontology. However, it is not possible to make a straightforward transformation of all elements from a domain ontology into a conceptual data model because ontology is semantically richer than data conceptual models.

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Domain knowledge reuse during requirements engineering

Cited by 6 publications

References 12 publications

Software and Systems Traceability

Software and Systems Traceability

Towards Ontology-Based Requirements Engineering for IoT-Supported Well-Being, Aging and Health

Maintaining web application: an ontology‐based reverse engineering approach

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