Constraint-based Evolutionary Testing of Autonomous Distributed Systems

Distributed software systems are characterized by increasing autonomy. They often have the capability to sense the environment and react to it, discover the presence of other systems and take advantage of their services, adapt and re-configure themselves in accordance with the internal as well as the global state. Testing this kind of systems is challenging, and systematic and automated approaches are still missing. We propose a novel evolutionary testing framework for autonomous distributed systems. In this framework, test cases are continuously generated and executed. Our current implementation of the framework provides two techniques for the automated, continuous generation of test cases: (1) random; (2) evolutionary-mutation. Preliminary experimental results, obtained on a case study, are encouraging and indicate that evolutionary testing can complement effectively the manual one.