Predicting testability of program modules using a neural network

J.M. Voas (1992) defines testability as the probability that a test case will fail if the program has a fault. It is defined in the context of an oracle for the test, and a distribution of test cases, usually emulating operations. Because testability is a dynamic attribute of software, it is very computation-intensive to measure directly. The paper presents a case study of real time avionics software to predict the testability of each module from static measurements of source code. The static software metrics take much less computation than direct measurement of testability. Thus, a model based on inexpensive measurements could be an economical way to take advantage of testability attributes during software development. We found that neural networks are a promising technique for building such predictive models, because they are able to model nonlinearities in relationships. Our goal is to predict a quantity between zero and one whose distribution is highly skewed toward zero. This is very difficult for standard statistical techniques. In other words, high testability modules present a challenging prediction problem that is appropriate for neural networks