Shared data analysis for multi-tasking real-time system testing

Memory corruption due to program faults is one of the most common failures in computer software. For software running in a sequential manner and for multi-tasking software with synchronized data accesses, it has been shown that program faults causing memory corruption can be detected by analyzing the relations between defines and uses of variables (DU coverage-based testing). However, using such methods in testing for memory corruption where globally shared data is accessed through asynchronous events will not be sufficient since they lack the possibility to analyse the cases where preemption of tasks may lead to interleaving failures. In this paper, we propose the use of a system level shared variable DU analysis of multi-tasking real-time software. By analyzing the temporal attributes of each access to globally shared data, our method handles asynchronous data accesses. When used in system-level testing, the result from the analysis can discover failures such as ordering, synchronization and interleaving failures. The result can also serve a as measure for coverage and complexity in data dependency at system level.