Test oracles based on artificial neural networks and info fuzzy networks: A comparative study

Abstract

One of the key software development activities that ensures software quality is software testing that needs automation due to scarcity of resources in software production; however, automating the testing process faces several issues especially those issues associated with automated test oracles. Test oracles offer simple and reliable sources of expected software behavior that guide testers to undertake the testing process and detect faults. This paper performs a comparative study on two existing test oracles using a black-box approach. We compare experimental studies, processes, and evaluation procedures reported so far. The two test oracles are Multi-network oracles based on ANNs, and IFN-based regression tester. ANN-based oracles have the capability of processing complex relationships while IFN is a test oracle that is limited to one functionality, although the test oracle performs best when applied for regression testing. The results obtained from existing experiments and evaluations, and they indicate that Multi-Network oracles have a better accuracy rate of 98.26% and a minimal misclassification error rate of 1.74% compared to the IFN regression tester. Consequently, Multi-network oracles based on ANNs are more suitable, offering better quality and reliability for a software testing process.