Assessing the Use of Machine Learning to Find the Worst-Case Execution Time of Avionics Software

FA8702-15-D-0002Many parts in aircraft today rely on software that interacts with its physical environment. Typically, this interaction involves taking sensor readings, sending actuation commands, reading commands from humans, and presenting information to humans. These interactions require that the software deliver results at the right time,\uf020as argued in the guidance document DO-178C and in previous FAA reports. Correct timing, in turn, depends on the execution time of individual programs. Hence, the problem of finding an upper bound on the execution time of a program,\uf020called Worst-Case Execution Time (WCET) analysis,\uf020is an important step in avionics certification. Unfortunately, WCET analysis is difficult for several reasons. A program can typically execute a large number of different paths. During the execution of one path, the program uses resources in a complex way and this resource use is difficult to analyze. Finally, during the execution of one path, the speed of execution depends on temperature, which, in turn, depends on earlier execution. This report presents research on WCET analysis using Machine Learning (ML) and Artificial Intelligence (AI) aiming to make WCET analysis less dependent on detailed knowledge of the program that is analyzed and the hardware used