Ontology-based automatic reasoning and NLP for tracing software requirements into models with the OntoTrace tool

Mosquera Tobón, Jose David; Ruiz, Marcela; Pastor, Oscar; Spielberger, Jürgen

doi:10.1007/978-3-031-29786-1_10

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30652

Full metadata record

DC Field	Value	Language
dc.contributor.author	Mosquera Tobón, Jose David	-
dc.contributor.author	Ruiz, Marcela	-
dc.contributor.author	Pastor, Oscar	-
dc.contributor.author	Spielberger, Jürgen	-
dc.date.accessioned	2024-05-16T11:31:25Z	-
dc.date.available	2024-05-16T11:31:25Z	-
dc.date.issued	2023-04-01	-
dc.identifier.isbn	978-3-031-29785-4	de_CH
dc.identifier.isbn	978-3-031-29786-1	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/30652	-
dc.description.abstract	Context and motivation. Traceability is an essential part of quality assurance tasks for software maintainability, validation, and verification. However, the effort required to create and maintain traces is still high compared to their benefits. Problem. Some authors have proposed traceability tools to address this challenge, yet some of those tools require historical traceability data to generate traces, representing an entry barrier to software development teams that do not do traceability. Another common requirement of existing traceability tools is the scope of artefacts to be traced, hindering the adaptability of traceability tools in practice. Principal ideas. Motivated by the mentioned challenges, in this paper we propose OntoTraceV2.0: a tool for supporting trace generation of arbitrary software artefacts without depending on historical traceability data. The architecture of OntoTraceV2.0 integrates ontology-based automatic reasoning to facilitate adaptability for tracing arbitrary artefacts and natural language processing for discovering traces based on text-based similarity between artefacts. We conducted a quasi-experiment with 36 subjects to validate OntoTraceV2.0 in terms of efficiency, effectiveness, and satisfaction. Contribution. We found that OntoTraceV2.0 positively affects the subjects’ efficiency and satisfaction during trace generation compared to a manual approach. Although the subjects’ average effectiveness is higher using OntoTraceV2.0, we observe no statistical difference with the manual trace generation approach. Even though such results are promising, further replications are needed to avoid certain threats to validity. We conclude the paper by analysing the experimental results and limitations we found, drawing on future challenges, and proposing the next research endeavours.	de_CH
dc.language.iso	en	de_CH
dc.publisher	Springer	de_CH
dc.relation.ispartofseries	Lecture Notes in Computer Science	de_CH
dc.rights	Licence according to publishing contract	de_CH
dc.subject	Traceability	de_CH
dc.subject	Ontology	de_CH
dc.subject	Automatic reasoning	de_CH
dc.subject	OntoTrace	de_CH
dc.subject	Natural language processing (NLP)	de_CH
dc.subject.ddc	005: Computerprogrammierung, Programme und Daten	de_CH
dc.subject.ddc	410.285: Computerlinguistik	de_CH
dc.title	Ontology-based automatic reasoning and NLP for tracing software requirements into models with the OntoTrace tool	de_CH
dc.type	Konferenz: Paper	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Informatik (InIT)	de_CH
zhaw.publisher.place	Cham	de_CH
dc.identifier.doi	10.1007/978-3-031-29786-1_10	de_CH
dc.identifier.doi	10.21256/zhaw-30652	-
zhaw.conference.details	29th International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ), Barcelona, Spain, 17-20th April 2023	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	158	de_CH
zhaw.pages.start	140	de_CH
zhaw.parentwork.editor	Ferrari, Alessio	-
zhaw.parentwork.editor	Penzenstadler, Birgit	-
zhaw.publication.status	acceptedVersion	de_CH
zhaw.series.number	13975	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.title.proceedings	Requirements Engineering: Foundation for Software Quality : REFSQ 2023	de_CH
zhaw.webfeed	Software Engineering	de_CH
zhaw.webfeed	Software Systems	de_CH
zhaw.funding.zhaw	Smart Hospital – Integrated Framework, Tools & Solutions (SHIFT)	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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Mosquera Tobón, J. D., Ruiz, M., Pastor, O., & Spielberger, J. (2023). Ontology-based automatic reasoning and NLP for tracing software requirements into models with the OntoTrace tool [Conference paper]. In A. Ferrari & B. Penzenstadler (Eds.), Requirements Engineering: Foundation for Software Quality : REFSQ 2023 (pp. 140–158). Springer. https://doi.org/10.1007/978-3-031-29786-1_10

Mosquera Tobón, J.D. et al. (2023) ‘Ontology-based automatic reasoning and NLP for tracing software requirements into models with the OntoTrace tool’, in A. Ferrari and B. Penzenstadler (eds) Requirements Engineering: Foundation for Software Quality : REFSQ 2023. Cham: Springer, pp. 140–158. Available at: https://doi.org/10.1007/978-3-031-29786-1_10.

J. D. Mosquera Tobón, M. Ruiz, O. Pastor, and J. Spielberger, “Ontology-based automatic reasoning and NLP for tracing software requirements into models with the OntoTrace tool,” in Requirements Engineering: Foundation for Software Quality : REFSQ 2023, Apr. 2023, pp. 140–158. doi: 10.1007/978-3-031-29786-1_10.

MOSQUERA TOBÓN, Jose David, Marcela RUIZ, Oscar PASTOR und Jürgen SPIELBERGER, 2023. Ontology-based automatic reasoning and NLP for tracing software requirements into models with the OntoTrace tool. In: Alessio FERRARI und Birgit PENZENSTADLER (Hrsg.), Requirements Engineering: Foundation for Software Quality : REFSQ 2023. Conference paper. Cham: Springer. 1 April 2023. S. 140–158. ISBN 978-3-031-29785-4

Mosquera Tobón, Jose David, Marcela Ruiz, Oscar Pastor, and Jürgen Spielberger. 2023. “Ontology-Based Automatic Reasoning and NLP for Tracing Software Requirements into Models with the OntoTrace Tool.” Conference paper. In Requirements Engineering: Foundation for Software Quality : REFSQ 2023, edited by Alessio Ferrari and Birgit Penzenstadler, 140–58. Cham: Springer. https://doi.org/10.1007/978-3-031-29786-1_10.

Mosquera Tobón, Jose David, et al. “Ontology-Based Automatic Reasoning and NLP for Tracing Software Requirements into Models with the OntoTrace Tool.” Requirements Engineering: Foundation for Software Quality : REFSQ 2023, edited by Alessio Ferrari and Birgit Penzenstadler, Springer, 2023, pp. 140–58, https://doi.org/10.1007/978-3-031-29786-1_10.