Towards Inductive Learning of Formal Software Architecture Rules

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dc.contributor.authorSchindler, Christian
dc.contributor.authorRausch , Andreas
dc.date.accessioned2023-12-26T18:53:24Z
dc.date.available2023-12-26T18:53:24Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.876
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other4fad9283-7e53-40ba-ad14-534ce0d62ed2
dc.identifier.urihttps://hdl.handle.net/10125/107262
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAI-based Methods and Applications for Software Engineering
dc.subjectconstraint learning
dc.subjectfirst order logic
dc.subjectinductive rule learning
dc.subjectsoftware architecture
dc.titleTowards Inductive Learning of Formal Software Architecture Rules
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThis paper explores the application of inductive learning for inferring software architecture rules from real-world systems. Traditional manual rule specification approaches are time-consuming and error-prone, motivating the need for automated rule discovery. Leveraging a dataset of software architecture instances and a metamodel capturing implementation facts, we train inductive learning algorithms to extract meaningful rules. The induced rules are evaluated against a predefined hypothesis and their generalizability across different system subsets is investigated. The research highlights the capabilities and limitations of inductive rule learning in the area of software architecture, aiming to inspire further innovation in data-driven rule discovery for more intelligent software architecture practices.
dcterms.extent10 pages
prism.startingpage7302

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