An application of explanation-based learning to protocol conformance testing

A method that employs a machine learning technique for the semiautomatic generation of protocol-conformance test-sequence requirements is described. Given a protocol knowledge representation and some high-level nonexecutable descriptions of protocol behavior, a learning algorithm based on extended explanation-based generalization produces conformance test-sequence requirements for a protocol implementation under test. The role of learning is to compile relevant parts of protocol knowledge into behaviors, consequently inferring executable protocol behaviors. This inference makes explicit constraints that are implicit in both the protocol knowledge and the behaviors. It is shown that the approach facilitates the derivation of new operational constraints on protocol behavior. The new constraints lead to new types of protocol behavior, thereby yielding potentially valuable new conformance test cases. An application of the method to the Alternating Bit Protocol (ABP) (a canonical example in protocols research literature) is described.<
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