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Article

Tags’ Recommender to Classify Architectural Knowledge Applying Language Models

1
Departamento de Computación y Diseño, Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Mexico
2
Department of Information Technologies, Universidad Tecnológica de Nogales, Nogales 84097, Mexico
3
Unidad Navojoa, Instituto Tecnológico de Sonora, Navojoa 85860, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Grigoreta-Sofia Cojocar and Adriana-Mihaela Guran
Mathematics 2022, 10(3), 446; https://doi.org/10.3390/math10030446
Received: 22 November 2021 / Revised: 21 December 2021 / Accepted: 24 December 2021 / Published: 30 January 2022
Agile global software engineering challenges architectural knowledge (AK) management since face-to-face interactions are preferred over comprehensive documentation, which causes AK loss over time. The AK condensation concept was proposed to reduce AK losing, using the AK shared through unstructured electronic media. A crucial part of this concept is a classification mechanism to ease AK recovery in the future. We developed a Slack complement as a classification mechanism based on social tagging, which recommends tags according to a chat/message topic, using natural language processing (NLP) techniques. We evaluated two tagging modes: NLP-assisted versus alphabetical auto-completion, in terms of correctness and time to select a tag. Fifty-two participants used the complement emulating an agile and global scenario and gave us their complement’s perceptions about usefulness, ease of use, and work integration. Messages tagged through NLP recommendations showed fewer semantic errors, and participants spent less time selecting a tag. They perceived the component as very usable, useful, and easy to be integrated into the daily work. These results indicated that a tag recommendation system is necessary to classify the shared AK accurately and quickly. We will improve the NLP techniques to evaluate AK condensation in a long-term test as future work. View Full-Text
Keywords: agile global software engineering; architectural knowledge management; natural language processing; knowledge condensing agile global software engineering; architectural knowledge management; natural language processing; knowledge condensing
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MDPI and ACS Style

Borrego, G.; González-López, S.; Palacio, R.R. Tags’ Recommender to Classify Architectural Knowledge Applying Language Models. Mathematics 2022, 10, 446. https://doi.org/10.3390/math10030446

AMA Style

Borrego G, González-López S, Palacio RR. Tags’ Recommender to Classify Architectural Knowledge Applying Language Models. Mathematics. 2022; 10(3):446. https://doi.org/10.3390/math10030446

Chicago/Turabian Style

Borrego, Gilberto, Samuel González-López, and Ramón R. Palacio. 2022. "Tags’ Recommender to Classify Architectural Knowledge Applying Language Models" Mathematics 10, no. 3: 446. https://doi.org/10.3390/math10030446

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