Learning Worst-Case Execution Time Loop Bounds with Inductive Logic Programming

Most development, verification and validation methods in software engineering require some form of model populated with appropriate information. Real-time systems are no exception. However a significant issue is the information needed is not always available. Often this information is derived using manual methods which is costly in terms of time and money. In this paper we show how techniques taken from other areas may provide more effective and efficient solutions. More specifically machine learning is used to automatically determine the loop bounds. The paper shows how taking an approach based on machine learning allows a difficult problem to be addressed with relative ease.