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Software reliability modeling using increased failure interval with ANN

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Abstract

In growing Software industry, whenever engineers developed new software, they want to make sure that it is failure free and reliable. With the increasing reliability of hardware and growing complexity of software, the software reliability is a rising concern for both developer and users. Software reliability is the key part of quality and customer satisfaction. For the last three decades, many software reliability models have been successfully utilized in practical software reliability engineering. However, no single model can obtain the accurate prediction for all cases. This paper proposed the software reliability model with the increased number of training data set and neural networks. The back propagation algorithm has been chosen and applied for a learning process. The obtained results show that the proposed model increases the accuracy of the software reliability prediction.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Study World College of Engineering, Coimbatore, India

    K. Kumaresan

  2. Department of Information Technology, Anna University Regional Campus, Coimbatore, India

    P. Ganeshkumar

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  1. K. Kumaresan
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  2. P. Ganeshkumar
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Correspondence to K. Kumaresan.

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Kumaresan, K., Ganeshkumar, P. Software reliability modeling using increased failure interval with ANN. Cluster Comput 22 (Suppl 2), 3095–3102 (2019). https://doi.org/10.1007/s10586-018-1942-4

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  • DOI: https://doi.org/10.1007/s10586-018-1942-4

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