Work in Progress: Early Timing Prediction of Real-Time Tasks in Continuous Integration Environments

Timing prediction for real-time systems, especially those based on multi-core processor technology, presents enor-mous challenges in the design of modern industrial control sys-tems. Classical timing analysis techniques that have been effective in the past cannot yet keep up with the increased complexity of industrial control systems. Therefore, using hardware-in-the-loop testing to understand the timing behavior of real-time tasks is a prevalent practice across many industries. However, applying this state-of-the-practice to Continuous Integration (CI) software development workflows is expensive, and frequently leads to delayed developer feedback on task timing for code commits. To address this challenge, we propose the Chronos framework, which focuses on improving development efficiency of industrial control system software. Chronos utilizes CI data from both simulation and hardware-in-the-loop testing to build machine learning based, cross-platform timing prediction models, which correlate the simulated performance of the tasks with their actual timing observed on the target embedded hardware. For any new code that is committed, the timing prediction is triggered by the CI server with the trained machine learning models, enabling fast feedback on timing behavior of the committed code. We demonstrate the effectiveness of Chronos with preliminary results on real industrial control system setups.