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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 8040346, 6 pages
https://doi.org/10.1155/2017/8040346
Research Article

Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

School of Computing, SRM University, Kattankulathur, Tamil Nadu 603203, India

Correspondence should be addressed to Indhurani Lakshmanan; moc.liamg@a.inaruhdni

Received 2 February 2017; Revised 22 June 2017; Accepted 27 June 2017; Published 26 July 2017

Academic Editor: Erik Cuevas

Copyright © 2017 Subburaj Ramasamy and Indhurani Lakshmanan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.