Software Testing as a Problem of Machine Learning: Towards a Foundation on Computational Learning Theory

Abstract

In recent years, the application of machine learning techniques to software testing has been an active research area. Among the most notable work reported in the literature are those experiments on the uses of supervised and semi-supervised learning techniques to develop test oracles so that the correctness of software outputs and behaviours on new test cases can be predicated. Experiment data show that it seems a promising approach to the test oracle automation problem. In general, software testing is an inductive inference in the course of which the tester attempts to deduce general properties of a software system by observing the behaviours of the system on a finite number of test cases. This talk discusses the theoretical foundation of software testing from the perspective of computational machine learning theories.