Abstract In component‐based software development, integration testing is a crucial step in verifying the composite behaviour of a system. However, very few formally or empirically validated approaches are available for systematically testing if components have been successfully integrated. In practice, integration testing of component‐based systems is usually performed in a time‐ and resource‐limited context, which further increases the demand for effective test selection strategies. In this work, we therefore analyse the relationship between different component and interface coupling measures found in literature and the distribution of failures found during integration testing of an automotive system. By investigating the correlation for each measure at two architectural levels, we discuss its usefulness to guide integration testing at the software component level as well as for the hardware component level where coupling is measured among multiple electronic control units (ECUs) of a vehicle. Our results indicate that there is a positive correlation between coupling measures and failure‐proneness at both architectural level for all tested measures. However, at the hardware component level, all measures achieved a significantly higher correlation when compared to the software‐level correlation. Consequently, we conclude that prioritizing testing of highly coupled components and interfaces is a valid approach for systematic integration testing, as coupling proved to be a valid indicator for failure‐proneness.

In the age of autonomously driving vehicles, functionality and complexity of embedded systems are increasing tremendously. Safety aspects become more important and require such systems to operate with the highest possible level of fault tolerance. Simulation and systematic testing techniques often reach their limits in this regard. Here, formal verification as a long established technique can be an appropriate complement. However, the necessary preparatory work like adequately modeling a system and specifying properties in temporal logic are anything but trivial. In this paper, we report on our experiences applying model checking to verify the arbitration logic of a Vehicle Control System. We balance pros and cons of different model checking techniques and tools, and reason about our choice of the symbolic model checker NuSMV. We describe the process of modeling the architecture, resulting in ~1500 LOC, 69 state variables and 38 LTL constraints. To handle this large-scale model, we automate and optimize the model checking procedure for use on multi-core CPUs and employ Bounded Model Checking to avoid the state explosion problem. We share our lessons learned and provide valuable insights for architects, developers, and test engineers involved in this highly present topic.

Modern automotive E/E systems are implemented as distributed real-time software systems. The constantly growing complexity of safety-relevant software functions leads to an increased importance of testing during system integration of such systems. Systematic metrics are required to guide the testing process during system integration by providing coverage measures and stopping criteria but few studied approaches exist. For this purpose, we introduce a data-flow based observation scheme which captures the interplay behavior of involved ECUs during test execution and failure occurrences. In addition, we introduce a data flow-based coverage criterion designed for black box integration. By applying the observation scheme to test cases and associated faults found during execution, we first analyze similarities in data flow coverage. By further analyzing the data flow of failures, that slipped through the phase of system integration testing, we evaluate the usefulness of test gaps identified by using the suggested coverage criterion. We found major differences in the usage of data flow between undetected failures and existing test cases. In addition, we found that for the studied system under test the occurrence of failures is not necessarily a direct consequence of the test execution due to functional dependencies and side effects. Overall, these findings highlight the potential and limitations of data flow-based measures to be formalized as coverage or stopping criteria for the integration testing of distributed software systems.

In this paper, we investigate content-centric data transmission in the context of short opportunistic contacts and base our work on an existing content-centric networking architecture. In case of short interconnection times, file transfers may not be completed and the received information is discarded. Caches in content-centric networks are used for short-term storage and do not guarantee persistence. We implemented a mechanism to extend caching on persistent storage enabling the completion of disrupted content transfers. The mechanisms have been implemented in the CCNx framework and have been evaluated on wireless mesh nodes. Our evaluations using multicast and unicast communication show that the implementation can support content transfers in opportunistic environments without significant processing and storing overhead.