Abstract
Software fault detection is an important factor for quantitatively characterizing software quality. One of the proposed methods for software fault detection is neural networks. Fault detection is actually a pattern recognition task. Faulty and fault free data are different patterns which must be recognized. In this paper we propose a new framework for modeling software testing and fault detection in applications. Recurrent neural network architecture is used to improve performance of the system. Based on the experiments performed on the software reliability data obtained from middle-sized application software, it is observed that the non-linear RNN can be effective and efficient for software faults detection.
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