Understanding intended behavior using models of low-level signals

As software systems increase in complexity and operate with less human supervision, it becomes more difficult to use traditional techniques to detect when software is not behaving as intended. Furthermore, many systems operating today are nondeterministic and operate in unpredictable environments, making it difficult to even define what constitutes correct behavior. I propose a family of novel techniques to model the behavior of executing programs using low-level signals collected during executions. The models provide a basis for predicting whether an execution of the program or program unit under test represents intended behavior. I have demonstrated success with these techniques for detecting faulty and unexpected behavior on small programs. I propose to extend the work to smaller units of large, complex programs.