Technical Paper
Methods for Prediction, Simulation and Verification of Real-Time Software Architectural Design based on Machine Learning Algorithms
2015-04-14
2015-01-0190
In embedded system software architectural design, the Real-Time (RT) behavior estimation needs special care and contains many technical challenges. Most of the current approaches depend on either the engineering judgment or the actual measurements that are performed during the integration-testing phase. Both approaches may cause errors that lead to violations in the RT constraints. Both approaches are not error proof and can yield to RT constraints violations discovered during simulation of RT architectural design or during product validation. Impact on project could even be a Central Processing Unit (CPU) change. In this work, Operating System (OS) process Execution Time (ET) is considered the basic element of RT architectural design. Each process ET is predicted based on previous software releases, using Machine Learning (ML) algorithms.
