Research Article Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function Subburaj Ramasamy and Indhurani Lakshmanan School of Computing, SRM University, Kattankulathur, Tamil Nadu 603203, India Correspondence should be addressed to Indhurani Lakshmanan; indhurani.a@gmail.com 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. his 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. Reliability is one of the quantiiable sotware quality attributes. Sotware Reliability Growth Models (SRGMs) are used to assess the reliability achieved at diferent times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test efort varies with time. To overcome this lacuna, test efort was used instead of time in SRGMs. In the past, inite test efort functions were proposed, which may not be realistic as, at ininite testing time, test efort will be ininite. Hence in this paper, we propose an ininite test efort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artiicial Neural Network (ANN) for training the proposed model with sotware 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 sotware failure data sets. he 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 sotware release time determination as well. 1. Introduction Early Sotware Reliability Growth Models (SRGMs) represent the relationship between the time to failure and the cumula- tive number of faults detected till then. Many such SRGMs have been proposed as parametric [1–14] and nonparametric [15–18] models since the year 1972 to estimate future failure occurrence times and assess the reliability growth of sotware systems during the testing phase. he traditional SRGMs are based on the premise that the mean value function of the model follows either exponential growth [1, 3] or S-shaped growth [2, 11] or both [4–8]. Some SRGMs have been proposed with testing efort function (TEF) [11–13], since the fault detection and cor- rection depend on eforts consumed such as test cases executed, man-days expended, computer utilization time, and other resources consumed, rather than only testing time or calendar time. he efort based SRGMs proposed in the past use exponential, Rayleigh, logistic, or Weibull distri- butions to specify testing efort function (TEF) to denote efort consumption during testing [11–13]. Although these functions seem to give good result and can well it in some cases, there is a fallacy in assuming inite total test efort at an ininite time. Xie and Zhao proposed a Nonhomogeneous Poisson Process (NHPP) reliability growth model based on log-power distribution which is a graphical model where itting of the data or not can be visualized in a graph before parameter estimation [19]. In this paper, we propose using log-power [19] distribu- tion to describe TEF in Goel and Okumoto [1] SRGM to provide an SRGM with ininite TEF. We use Artiicial Neural Network (ANN) for parameter estimation and apply machine learning technique to determine the most suitable weights for the proposed model that will it the past and future data equally well. We study and compare the goodness of it (GoF) performance of the proposed model with a popular test efort function based SRGM. We use ANN for parameter estima- tion uniformly in all cases since ANN improves the parameter estimation accuracy and gives better goodness of it rather than traditional statistical parametric models [15–18]. Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 8040346, 6 pages https://doi.org/10.1155/2017/8040346