research-article

Recommendation of UML Model Conflicts: Unveiling the Biometric Lens for Conflict Resolution

Authors:

Guilherme Dalcin,

Willian Bolzan,

Kleinner FariasAuthors Info & Claims

SBES '23: Proceedings of the XXXVII Brazilian Symposium on Software Engineering

Pages 83 - 88

https://doi.org/10.1145/3613372.3613378

Published: 25 September 2023 Publication History

Abstract

Model merging assumes a pivotal role in numerous model-centric software development tasks, e.g., evolving UML models to add new features or even reconciling UML models developed collaboratively by distributed development teams. Usually, UML model elements to-be-merged conflict with each other. Unfortunately, resolving conflicts remains a highly cognitive and error-prone task. Today, wearable devices capable of capturing biometric data are a reality. Recent studies indicate that the developer’s cognitive indicators may affect developers while performing development tasks. However, the current literature has neglected the recommendation of conflicts sensitive to the cognitive activities of software developers. This study, therefore, introduces BACR, a biometric-aware approach to recommend UML model conflicts using machine learning. BACR helps UML model merging to push a step forward, recommending model conflicts based on appropriate biometric indicators and using a behavior sequence transformer model. Our approach is based on four scientific institutions. It represents the first effort in supporting the prioritization of cognitively relevant UML model conflicts by developers, mitigating the risk of making incorrect decisions and preventing potential downstream issues.

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Cited By

Marco VFarias K(2024)Exploring the Technologies and Architectures Used to Develop Micro-frontend Applications: a Systematic Mapping and Emerging PerspectivesSSRN Electronic Journal10.2139/ssrn.4750661Online publication date: 2024
https://doi.org/10.2139/ssrn.4750661
Marcén AIglesias ALapeña RPérez FCetina C(2024)A Systematic Literature Review of Model-Driven Engineering Using Machine LearningIEEE Transactions on Software Engineering10.1109/TSE.2024.343051450:9(2269-2293)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3430514

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cover image ACM Other conferences

SBES '23: Proceedings of the XXXVII Brazilian Symposium on Software Engineering

September 2023

570 pages

ISBN:9798400707872

DOI:10.1145/3613372

Copyright © 2023 ACM.

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Publication History

Published: 25 September 2023

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Research-article
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Refereed limited

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) award number(s):314248/2021-8

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SBES 2023

SBES 2023: XXXVII Brazilian Symposium on Software Engineering

September 25 - 29, 2023

Campo Grande, Brazil

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Cited By

Marco VFarias K(2024)Exploring the Technologies and Architectures Used to Develop Micro-frontend Applications: a Systematic Mapping and Emerging PerspectivesSSRN Electronic Journal10.2139/ssrn.4750661Online publication date: 2024
https://doi.org/10.2139/ssrn.4750661
Marcén AIglesias ALapeña RPérez FCetina C(2024)A Systematic Literature Review of Model-Driven Engineering Using Machine LearningIEEE Transactions on Software Engineering10.1109/TSE.2024.343051450:9(2269-2293)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3430514

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