Location

Hilton Hawaiian Village, Honolulu, Hawaii

Event Website

https://hicss.hawaii.edu/

Start Date

3-1-2024 12:00 AM

End Date

6-1-2024 12:00 AM

Description

This 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.

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Jan 3rd, 12:00 AM Jan 6th, 12:00 AM

Towards Inductive Learning of Formal Software Architecture Rules

Hilton Hawaiian Village, Honolulu, Hawaii

This 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.

https://aisel.aisnet.org/hicss-57/st/software_engineering/3