Automated System-Level Software Testing of Industrial Networked Embedded Systems

Sammanfattning: 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. Out of the many challenges for successful software test automation, this thesis addresses five: (i) understanding how updated software reaches a test environment, how testing is conducted in the test environment, and how test results reach the developers that updated the software in the first place; (ii) selecting which test cases to execute in a test suite given constraints on available time and test systems; (iii) given that the test cases an run on different configurations of connected devices, selecting which hardware to use for each test case to be executed; (iv) analyzing test cases that, when executed over time on evolving software, testware or hardware revisions, appear to randomly fail; and (v) making test results information actionable with test results exploration and visualization.The challenges are tackled in several ways. First, to better understand the flow of information in the embedded systems software development process, interviews at five different companies were conducted. The results show 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 in the test environment, and back to developers. Second, in order to address the challenges of test selection and hardware selection, automated approaches for testing given resource constraints were implemented and evaluated using industrial data stemming from years of nightly testing. It was shown that these approaches could solve problems such as nightly testing not finishing on time, as well as increasing hardware coverage by varying hardware selection over test iterations. Third, the challenge of intermittently failing tests was addressed with a new metric that can classify test cases as intermittently or consistently failing. Again, by using industry data, factors that lead to intermittent failures were identified, and similarities and differences between root causes for intermittently and consistently failing tests were observed. Finally, in order to better render test results actionable, a tool was implemented for test results exploration and visualization. The implementation was evaluated using a reference group and logging of the tool’s usage. Solution patterns and views of the tool were identified, as well as challenges for implementing such a tool.