Software testing, which once was a distinct phase in software development life cycle, has now become a parallel activity. Many researchers in the past have attributed the failure of software to the lack of adequate testing. Software testing involves checking whether the actual outputs generated by the SUT matches the expected outputs. Test cases are written and executed and the results are compared with the help of a test oracle. A Test Oracle is a mechanism to determine whether a test has passed or failed. The process of finding a reliable test oracle is called the oracle problem. Software test automation has been a hot area of research for more than a decade. But, the work in the area of test oracle automation is minimal. Some of these researches have proposed solutions for test oracle automation using machine learning algorithms like Genetic Algorithms (GA) and Artificial Neural Networks (ANN). In this paper, we present a brief review and comparative analysis of the use of single-network and multi network ANNs as test oracles.

Software Testing, Artificial Neural Networks, Test Oracles, Machine Learning, SDLC

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