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At the Universidad Politecnica de Madrid students attending a course on concurrency are taught a high-level formalism which permits concise specification of shared resources. This formalism is used to express safety-critical access policies for typical control problems such as robot plants. Students are moreover provided with programming recipes for implementing such shared resource specifications in programming languages (typically Java). The teachers of the course use various tools to ensure that the implementations developed by students for a shared resource are of an acceptable quality. Such tools include normal unit tests, but also the systematic application of property-based testing to judge the quality of the exercises. In this article we provide an overview of the tools, techniques and methods used in one particular exercise of the course: the implementation of a control system for an automated warehouse.

Testing is a crucial aspect of the development of dependable embedded systems, and therefore a significant effort is put into researching and developing efficient testing techniques. However, testing is not normally taught in specific courses at many universities, but rather as a peripheral activity to programming. In this paper, we report on three separate experiences at teaching an advanced testing technique, property-based testing, and a supporting tool, QuviQ Quick Check, to both undergraduate and master students.

This article describes a systematic approach to testing behavioural aspects of Web Services that communicate using the JSON data format. As a key component, the Quviq QuickCheck property-based testing tool is used to automatically generate a large number of test cases from an abstract description of the service behaviour in the form of a finite state machine. The same behavioural description is also used to decide whether the execution of a test case is successful or not. To generate random JSON data for populating tests we have developed a new library, jsongen, which given a characterisation of the JSON data as a JSON schema, automatically derives a QuickCheck generator which is capable of generating an infinite number of JSON values that validate against the schema.