Considering Not-Quantified Quality Attributes in an Automated Design Space Exploration

Abstract

In a software design process, the quality of the resulting software system is highly driven by the quality of its software architecture (SA). In such a process trade-off decisions must be made between multiple quality attributes (QAs), such as performance or security, that are often competing. Several approaches exist to improve SAs either quantitatively or qualitatively. The first group of approaches requires to quantify each single QA to be considered in the design process, while the latter group of approaches are often fully manual processes. However, time and cost constraints often make it impossible to either quantify all relevant QAs or manually evaluate candidate architectures. Our approach to the problem is to quantify several most important quality requirements, combine them with several not-quantified QAs and use them together in an automated design space exploration process. As our basis, we used the PerOpteryx design space exploration approach, which requires quantified measures for its optimization engine, and extended it in order to combine them with not-quantified QAs. By this, our approach allows optimizing the design space by considering even QAs that can not be quantified due to cost constraints or lack of quantification methodologies. We applied our approach to two case studies to demonstrate its benefits. We showed how performance can be balanced against not-quantified QAs, such as security, using an example derived from an industry case study.