Abstract
Design of software architecture is intellectually one of the most demanding tasks in software engineering. This paper proposes an approach to automatically synthesize software architecture using genetic algorithms. The technique applies architectural patterns for mutations and quality metrics for evaluation, producing a proposal for a software architecture on the basis of functional requirements given as a graph of functional responsibilities. Two quality attributes, modifiability and efficiency, are considered. The behavior of the genetic synthesis process is analyzed with respect to quality improve ment speed, the effect of dynamic mutation, and the effect of quality attribute prioritization. Our tests show that it is possible to genetically synthesize architectures that achieve a high fitness value early on.
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Räihä, O., Koskimies, K., Mäkinen, E. (2008). Genetic Synthesis of Software Architecture. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_57
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DOI: https://doi.org/10.1007/978-3-540-89694-4_57
Publisher Name: Springer, Berlin, Heidelberg
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