Active continuous quality control

Abstract

We present Active Continuous Quality Control (ACQC), a novel approach that employs incremental active automata learning technology periodically in order to infer evolving behavioral automata of complex applications accompanying the development process. This way we are able to closely monitor and steer the evolution of applications throughout their whole life-cycle with minimum manual effort. Key to this approach is to establish a stable level for comparison via an incrementally growing behavioral abstraction in terms of a user-centric communication alphabet: The letters of this alphabet, which may correspond to whole use cases, are intended to directly express the functionality from the user perspective. At the same time their choice allows one to focus on specific aspects, which establishes tailored abstraction levels on demand, which may be refined by adding new letters in the course of the systems evolution. This way ACQC does not only allow us to reveal serious bugs simply by inspecting difference views of the (tailored) models, but also to visually follow and control the effects of (intended) changes, which complements our model-checking-based quality control. All this will be illustrated along real-life scenarios that arose during the component-based development of a commercial editorial system.