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The influence of search components and problem characteristics in early life cycle class modelling

Smith, Jim; Simons, Chris

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Jim Smith
Professor in Interactive Artificial Intelligence


© 2014 Elsevier Inc. All rights reserved. This paper examines the factors affecting the quality of solution found by meta-heuristic search when optimising object-oriented software class models. From the algorithmic perspective, we examine the effect of encoding, choice of components such as the global search heuristic, and various means of incorporating problem- and instance-specific information. We also consider the effect of problem characteristics on the (estimated) cost of the global optimum, and the quality and distribution of local optima. The choice of global search component appears important, and adding problem and instance-specific information is generally beneficial to an evolutionary algorithm but detrimental to ant colony optimisation. The effect of problem characteristics is more complex. Neither scale nor complexity have a significant effect on the global optimum as estimated by the best solution ever found. However, using local search to locate 100,000 local optima for each problem confirms the results from meta-heuristic search: there are patterns in the distribution of local optima that increase with scale (problem size) and complexity (number of classes) and will cause problems for many classes of meta-heuristic search.


Smith, J., & Simons, C. (2015). The influence of search components and problem characteristics in early life cycle class modelling. Journal of Systems and Software, 103, 440-451.

Journal Article Type Conference Paper
Acceptance Date Nov 15, 2014
Online Publication Date Nov 29, 2014
Publication Date 2015-05
Deposit Date Nov 17, 2014
Publicly Available Date Nov 15, 2016
Journal Journal of Systems and Software
Print ISSN 0164-1212
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 103
Pages 440-451
Keywords evolutionary algorithms, class modelling, search-based software engineering, ant colony optimisation, memetic algorithms
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Additional Information Additional Information : NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Systems and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published online at


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