Multi-core timing analysis is one of the biggest challenges in Computer Science and especially in real-time systems. Multi-core timing analysis is not determining the Worst-Case Execution Time (WCET) of a software task  but instead building models of how much contention over shared resources can increase execution times . Appropriate solutions are also needed soon by industry. The academic challenges come from the breadth of knowledge needed to address the problem.
Detailed knowledge of computer architectures, real-time systems and software testing need to be applied to statistical analysis and machine learning. More precisely blindly applying current machine learning techniques will not be successful not least as understanding the validity of the approaches is as appropriate for certification as is an accurate results . Equally significant attention needs to be given that the data from testing addresses significant challenges from  including that the data and subsequent models are both sufficiently accurate and representative.
The proposed research would address the following using a test rig of Raspberry PI3s and real software from a Rolls-Royce aircraft engine.
1. Development of appropriate systematic software testing techniques that give confidence in the results building on the principles in  and  but deleting with the key challenges in .
2. Combined statistical and machine learning techniques that select the data to be monitoring and make accurate predictions of the execution time inflation based on contention over shared processing resources .
3. Explanations of the inaccuracies in the predictions and guidance for further testing [3,4,5].
4. Techniques for establishing the validity of both the noisy data and the resulting predictions [5, 6].
5. Schedulability analysis techniques based on the results of machine learning instead of single values for the WCET analysis .
This is a self funded project, there is no funding attached to it.
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