Evolutionary robustness testing of data processing systems using models and data mutation

System level testing of industrial data processing software poses several challenges. Input data can be very large, even in the order of gigabytes, and with complex constraints that define when an input is valid. Generating the right input data to stress the system for robustness properties (e.g. to test how faulty data is handled) is hence very complex, tedious and error prone when done manually. Unfortunately, this is the current practice in industry. In previous work, we defined a methodology to model the structure and the constraints of input data by using UML class diagrams and OCL constraints. Tests were automatically derived to cover predefined fault types in a fault model. In this paper, to obtain more effective system level test cases, we developed a novel search-based test generation tool. Experiments on a real-world, large industrial data processing system show that our automated approach can not only achieve better code coverage, but also accomplishes this using significantly smaller test suites.