Automated Test Oracle Based on Neural Networks
Mao Ye, Boqin Feng, Li Zhu, Yao Lin · 2006
In this paper an attempt has been made to explore the possibility of the usage of artificial neural networks as automated test oracle. Automated test oracle includes capabilities to generate expected output and compare it with actual output automatically. It is important for automated software testing. But there are very few techniques to implement it. In this paper, an insensitive oracle is proposed. It generates approximate output that is close to expected output. The actual output is then compared with the approximate output in an interval. The relation between inputs and outputs of an application under testing is described as a function. When it is a continue function, neural networks are used to estimate the output after training. By the method, automated oracle can be implemented and precision be adjusted by parameters. It can save a lot of time and labor in software testing