A Comparative Study of Artificial Neural Networks and Info-Fuzzy Networks as Automated Oracles in Software Testing

作者：Agarwal Deepam^*; Tamir Dan E; Last Mark; Kandel Abraham

来源：IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans , 2012, 42(5): 1183-1193.

DOI：10.1109/TSMCA.2012.2183590

• Black-box testing
• clustering techniques
• dispersion analysis
• info-fuzzy networks (IFNs)
• neural networks
• software testing

Software quality is one of the main concerns of software users. Hence, software testing is an utterly important phase in the software development life cycle. Nevertheless, manual evaluation of program compliance with its specification may be prohibitively time consuming. As a remedy, several software testing systems are using an automatic oracle to confirm that the developed software complies with its specification and determine whether a given test case exposes faults. The use of artificial neural networks and info-fuzzy networks as automated oracles has been explored elsewhere. Nevertheless, there is not enough research comparing these two popular approaches to automated evaluation of the test outcome. This paper fills the gap and reports on a set of experiments designed to compare the two methods based on ROC curves, training time, and dispersion analysis.

• 出版日期2012-9