Automated System Level Software Testing of Networked Embedded Systems

Embedded systems are ubiquitous and play critical roles in management systems for industry and transport. Software failures in these domains may lead to loss of production or even loss of life, so the software in these systems needs to be reliable. Software testing is a standard approach for quality assurance of embedded software, and many software development processes strive for test automation. However, important challenges for successful software test automation are: lack of time for testing, lack of test environment availability, and an excess of test results information that renders decision-making hard. In this thesis these challenges are tackled in three ways. First, in order to combat lack of time for testing, a method for automated system level regression test selection was implemented and evaluated using data from several years of nightly testing in a real world industrial setting. It was shown that automated test selection can be integrated into system level nightly testing and solve problems such as nightly testing not finishing on time. Second, in order to improve the hardware coverage of devices in the test environment and how test cases map to hardware, an algorithm based on the subgraph isomorphism problem was implemented and evaluated using industrial data. This implementation was significantly faster than the previous implementation, and the mapping process was done in such a way that hardware coverage increased over iterations. Third, to better understand decision-making in critical steps of the software development process in an industrial setting, two empirical studies were conducted. The results showed how visualizations and a test results database support decision-making. Results also describe the overall flow of information in software testing: from developers to hardware, and back to developers via the test results database. Automated system level software testing of networked embedded systems can be difficult to achieve. This thesis addresses several important challenges and provides results that are of interest both to industrial practitioners and researchers