Rafael Olaechea, PhD candidate
David R. Cheriton School of Computer Science
A key objective of self-adaptive systems is to continue to provide optimal quality of service when the environment changes. A dynamic software product line (DSPL) can benefit from knowing how its various product variants would have performed (in terms of quality of service) with respect to the recent history of inputs.
We propose a family-based analysis that simulates all the product variants of a DSPL simultaneously, at runtime, on recent environmental inputs to obtain an estimate of the quality of service that each one of the product variants would have had, provided it had been executing. We assessed the efficiency of our DSPL analysis compared to the efficiency of analyzing each product individually on three case studies. We obtained mixed results due to the explosion of quality-of-service values for the product variants of a DSPL. After introducing a simple data abstraction on the values of quality-of- service variables, our DSPL analysis is between 1.4 and 7.7 times faster than analyzing the products one at a time.
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