Risk-based testing via active continuous quality control

Abstract

In this paper, we show how our approach to active continuous quality control (ACQC), which employs learning technology to automatically maintain test models along the whole life cycle, can be extended to include risk analysts for supporting risk-based testing. Key to this enhancement is the tailoring of ACQC's characteristic automata learning-based model extraction to prioritize critical aspects. Technically, risk analysts are provided with an abstract modeling level tailored to design test components (learning symbols) that encompass data-flow constraints reflecting a given risk profile. The resulting alphabet models are already sufficient to steer the ACQC process in a fashion that increases the risk coverage, while it at the same time radically reduces the testing effort. We illustrate our approach by means of case studies with Springer's Online Conference Service (OCS) which show the impact of the risk prioritization on the performance: risk-based regression testing tailored for system migration and for pure functional evolution.