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

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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.

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Notes

  1. The use of anthropocentric language is deliberate here, and mimics users’ vocal responses during interaction sessions.

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Acknowledgments

The authors would like to very much thank the editorial staff and the anonymous reviewers for their professional and constructive comments.

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Authors and Affiliations

  1. Department of Computer Science and Creative Technologies, University of the West of England, Bristol , BS16 1QY, UK

    Christopher L. Simons, Jim Smith & Paul White

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  1. Christopher L. Simons
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  2. Jim Smith
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Correspondence to Christopher L. Simons.

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Simons, C.L., Smith, J. & White, P. Interactive ant colony optimization (iACO) for early lifecycle software design. Swarm Intell 8, 139–157 (2014). https://doi.org/10.1007/s11721-014-0094-2

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