Interactive ant colony optimization (iACO) for early lifecycle software design

Simons, Chris; Smith, Jim; White, Paul

Abstract

Finding good designs in the early stages of the software development lifecycle is a demanding multi-objective problem that is crucial to success. Previously, both interactive and non-interactive techniques based on evolutionary algorithms (EAs) have been successfully applied to assist the designer. However, recently ant colony optimization was shown to outperform EAs at optimising quantitative measures of software designs with a limited computational budget. In this paper, we propose a novel interactive ACO (iACO) approach, in which the search is steered jointly by an adaptive model that combines subjective and objective measures. Results show that iACO is speedy, responsive and effective in enabling interactive, dynamic multi-objective search. Indeed, study participants rate the iACO search experience as compelling. Moreover, inspection of the learned model facilitates understanding of factors affecting users' judgements, such as the interplay between a design's elegance and the interdependencies between its components. © 2014 Springer Science+Business Media New York.

• Journal Article Type: Article
• Acceptance Date: May 30, 2014
• Online Publication Date: Jun 19, 2014
• Publication Date: Jun 30, 2014
• Publicly Available Date: Jun 6, 2019
• Journal: Swarm Intelligence
• Print ISSN: 1935-3812
• Electronic ISSN: 1935-3820
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 2
• Pages: 139-157
• DOI: https://doi.org/10.1007/s11721-014-0094-2
• Keywords: ant colony optimization, software design, interactive search
• Public URL: https://uwe-repository.worktribe.com/output/817012
• Publisher URL: http://dx.doi.org/10.1007/s11721-014-0094-2
• Additional Information: Additional Information : The final publication is available at Springer via http://dx.doi.org/10.1007/s11721-014-0094-2