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Software design is crucial to successful software development, yet is ademanding multi-objective problem for software engineers. In an attempt toassist the software designer, interactive (i.e. human in-the-loop)meta-heuristic search techniques such as evolutionary computing have beenapplied and show promising results. Recent investigations have also shown thatAnt Colony Optimization (ACO) can outperform evolutionary computing as apotential search engine for interactive software design. With a limitedcomputational budget, ACO produces superior candidate design solutions in asmaller number of iterations. Building on these findings, we propose a novelinteractive ACO (iACO) approach to assist the designer in early lifecyclesoftware design, in which the search is steered jointly by subjective designerevaluation as well as machine fitness functions relating the structuralintegrity and surrogate elegance of software designs. Results show that iACO isspeedy, responsive and highly effective in enabling interactive, dynamicmulti-objective search in early lifecycle software design. Study participantsrate the iACO search experience as compelling. Results of machine learning offitness measure weightings indicate that software design elegance does indeedplay a significant role in designer evaluation of candidate software design. Weconclude that the evenness of the number of attributes and methods amongclasses (NAC) is a significant surrogate elegance measure, which in turnsuggests that this evenness of distribution, when combined with structuralintegrity, is an implicit but crucial component of effective early lifecyclesoftware design.

Interactive Ant Colony Optimisation (iACO) for Early Lifecycle Software Design