Automated continuous quality assurance

Abstract

We present a case study that illustrates the power of active learning for enabling the automated quality assurance of complex and distributed evolving systems. We illustrate how the development of the OCS, Springer Verlag's Online Conference System, is supported by continuous learning-based testing, that by its nature maintains the synchrony of the running application and the learned (test) model. The evolution of the test model clearly indicates which portions of the system remain stable and which are altered. Thus our approach includes classical regression testing and feature interaction detection. We show concretely how model checking, automata learning, and quantitative analysis concur with the holistic quality assurance of this product.