Introducing Interactions in Multi-Objective Optimization of Software Architectures

• Published in: ACM transactions on software engineering and methodology Vol. 34; no. 6; pp. 1 - 39
• Main Authors: Cortellessa, Vittorio, Diaz-Pace, Jorge Andrés, Di Pompeo, Daniele, Frank, Sebastian, Jamshidi, Pooyan, Tucci, Michele, van Hoorn, André
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 01.07.2025
• Subjects: Evolutionary algorithms; Model-driven software engineering; Software and its engineering; Software evolution; Software performance; Software reliability; Theory of computation; interaction; refactoring; non-functional attributes; optimization; search-based software engineering
• ISSN: 1049-331X; 1557-7392
• DOI: 10.1145/3712185

Software architecture optimization aims to enhance non-functional attributes like performance and reliability while meeting functional requirements. Multi-objective optimization employs metaheuristic search techniques, such as genetic algorithms, to explore feasible architectural changes and propose alternatives to designers. However, this resource-intensive process may not always align with practical constraints. This study investigates the impact of designer interactions on multi-objective software architecture optimization. Designers can intervene at intermediate points in the fully automated optimization process, making choices that guide exploration towards more desirable solutions. Through several controlled experiments as well as an initial user study (14 subjects), we compare this interactive approach with a fully automated optimization process, which serves as a baseline. The findings demonstrate that designer interactions lead to a more focused solution space, resulting in improved architectural quality. By directing the search toward regions of interest, the interaction uncovers architectures that remain unexplored in the fully automated process. In the user study, participants found that our interactive approach provides a better trade-off between sufficient exploration of the solution space and the required computation time.