Towards composition of verified hardware devices

Computers are being used where no affordable level of testing is adequate. Safety and life critical systems must find a replacement for exhaustive testing to guarantee their correctness. Through a mathematical proof, hardware verification research has focused on device verification and has largely ignored system composition verification. To address these deficiencies, we examine how the current hardware verification methodology can be extended to verify complete systems

A test case generation framework based on UML statechart diagram

Early software fault detection offers more flexibility to correct errors in the early development stages. Unfortunately, existing studies in this domain are not sufficiently comprehensive in describing the major processes of the automated test case generation. Furthermore, the algorithms used for test case generation are not provided or well described. Current studies also hardly address loops and parallel paths issues, and achieved low coverage criteria. Therefore, this study proposes a test case generation framework that generates minimized and prioritized test cases from UML statechart diagram with higher coverage criteria. This study, conducted a review of the previous research to identify the issues and gaps related to test case generation, model-based testing, and coverage criteria. The proposed framework was designed from the gathered information based on the reviews and consists of eight components that represent a comprehensive test case generation processes. They are relation table, relation graph, consistency checking, test path minimization, test path prioritization, path pruning, test path generation, and test case generation. In addition, a prototype to implement the framework was developed. The evaluation of the framework was conducted in three phases: prototyping, comparison with previous studies, and expert review. The results reveal that the most suitable coverage criteria for UML statechart diagram are all-states coverage, all-transitions coverage, alltransition-pairs coverage, and all-loop-free-paths coverage. Furthermore, this study achieves higher coverage criteria in all coverage criteria, except for all-state coverage, when compared with the previous studies. The results of the experts’ review show that the framework is practical, easy to implement due to it is suitability to generate the test cases. The proposed algorithms provide correct results, and the prototype is able to generate test case effectively. Generally, the proposed system is well accepted by experts owing to its usefulness, usability, and accuracy. This study contributes to both theory and practice by providing an early alternative test case generation framework that achieves high coverage and can effectively generate test cases from UML statechart diagrams. This research adds new knowledge to the software testing field, especially for testing processes in the model-based techniques, testing activity, and testing tool support

On Provably Correct Decision-Making for Automated Driving

The introduction of driving automation in road vehicles can potentially reduce road traffic crashes and significantly improve road safety. Automation in road vehicles also brings several other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Many different hardware and software components (e.g. sensing, decision-making, actuation, and control) interact to solve the autonomous driving task. Correctness of such automated driving systems is crucial as incorrect behaviour may have catastrophic consequences. Autonomous vehicles operate in complex and dynamic environments, which requires decision-making and planning at different levels. The aim of such decision-making components in these systems is to make safe decisions at all times. The challenge of safety verification of these systems is crucial for the commercial deployment of full autonomy in vehicles. Testing for safety is expensive, impractical, and can never guarantee the absence of errors. In contrast, formal methods, which are techniques that use rigorous mathematical models to build hardware and software systems can provide a mathematical proof of the correctness of the system. The focus of this thesis is to address some of the challenges in the safety verification of decision-making in automated driving systems. A central question here is how to establish formal verification as an efficient tool for automated driving software development.A key finding is the need for an integrated formal approach to prove correctness and to provide a complete safety argument. This thesis provides insights into how three different formal verification approaches, namely supervisory control theory, model checking, and deductive verification differ in their application to automated driving and identifies the challenges associated with each method. It identifies the need for the introduction of more rigour in the requirement refinement process and presents one possible solution by using a formal model-based safety analysis approach. To address challenges in the manual modelling process, a possible solution by automatically learning formal models directly from code is proposed