Application of Search-Based Software Testing in Stress-Testing of Deeply Embedded Components in Integrated Circuits

Modern Integrated Circuits (ICs) based system design have evolved over time to support the ever demanding complex use-cases. As more functionality is packed into these ICs, there are more deeply embedded components present in these ICs. The performance of these deeply embedded components are critical in ensuring the final system behaviour. By virtue of being deeply embedded they are intrinsically difficult to test - especially in system context. This thesis is focused on the task of `stress testing' an IC in its operational environment with the goal of identifying any circumstances under which the circuit might suffer from performance issues. It addresses how search-based algorithms provide an automated approach in testing these ICs. Here it establishes that with a suitable search-space representation search-based approaches can be applied in stress-testing of complex ICs. Here it outlines how a simple hill-climbing based search algorithm is successfully used to generate tests in an automated manner. It also studies the application of more advanced search-based algorithms like random-restart hill-climbing and simulated annealing in addressing this testing challenge. As system behaviour of complex ICs can be affected in multiple ways, this research investigates the application of multiobjective algorithms in this context. It shows that a multi-objective algorithm like Strength Pareto Evolutionary Algorithm 2 (SPEA2) is able to generate better stress tests, maximising multiple objectives. The empirical studies performed as part of this thesis are conducted on ArmR Cache Coherent Interconnect (CCI), which provides cache coherency and interconnect functionality on modern Arm based systems. It concludes that search-based software testing approaches are indeed suitable for addressing testability challenges posed by modern ICs especially at system level and provide ways to generate better stress tests.