Predicting testability of program modules using a neural network

Voas 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. This 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 tale 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.