Constraint-Based Testing and Tail Tests for Measurement-Based Probabilistic Timing Analysis

The Worst-Case Execution Time (WCET) of tasks is an important data to give confidence that Real Time Systems will meet its timing requirements. Unfortunately, due to its tractability, this data is generally unknown. Measurement-Based Timing Analysis (MBTA), which relies on observing execution times driven by test data, has become a promising approach in the recent years. Some of the current testing techniques may take a relative long time at triggering decisions because they require very specific data of the input space. Conversely, Constraint-Based Testing (CBT) can cope better with these decisions as well as being more eficient. State-of-the-art approaches integrating CBT with MBTA have applied code coverage metrics designed for functional testing but not for WCET. Therefore, important functions such as generating test data for a path potentially leading to a large execution time are omitted. A central contribution of this work embraces CBT. Its objective is to meet code coverage needs for WCET. The evaluation compares this approach to state-of-theart Search-Based Testing (SBT) in MBTA and Random Testing (RT) methods and shows that, in most cases, CBT not only does not underestimate the largest observed execution time but also it achieves this data earlier. A downside of MBTA is that it normally underestimates the WCET. To face this issue execution time data is recently combined with probabilistic models. The current probabilistic protocol is hard to automate. Others probabilistic approaches have found alternative ways to achieve similar results automatically. The second main contribution aims for integrating this latter protocol and evaluating its applicability. The evaluation, which uses execution time data from test generators, shows that SBT and RT are more likely to generate data that enable this new approach. The WCET predictions are found more accurate by definition.