Syntax-Based Dependency Discovery : Extracting Dependencies Between Integration Test Cases for Passive Testing

Abstract: Modern-day vehicles consist of numerous electronic computing devices with accompanying software. Since vehicles are generally classified as safety-critical systems, rigorous testing strategies have to be deployed to ensure correct operation of the embedded software. Testing in an active sense is understood to be the main testing method for software in general. The main characteristic of active testing is that test cases have complete control over the system under test, warranting sequential execution of test cases. To complement active testing a method called passive testing is being researched, where the main benefit compared to active testing is that the evaluation of test cases becomes parallelizable. As passive testing opens up for concurrency, the issue of not recognizing faulty behavior arises. This could be the case when simultaneously testing functions which share an output, and the incorrect behavior of a function is being masked by the correct behavior of another function. To avoid vacuous test results, the evaluation of dependent test cases can be separated. Previous work describes the process of extracting dependencies between test cases from requirement specifications. However, this approach is not suitable when test cases are derived from various artifacts. In this thesis we present a syntax-based approach for dependency discovery between test cases. The approach was evaluated through a case study using integration test cases developed for active hardware-in-the-loop testing at Scania CV. Dependencies between 946 test cases have been extracted and the test cases were grouped into three categories, with 286 test cases being identified as independent. The approach shows some potential for evaluating test cases written for active testing, but the results indicate that using test cases written specifically for passive testing may yield better results. The approach also proved to be useful for finding bugs and deviations in scripts. Thus, the method has proven useful for discovering dependencies from syntax, but the results are strongly dependent on the complexity and commonality of the scripts.