An S-shaped software reliability model with imperfect debugging and improved testing learning process
04 Nov 2013-International Journal of Reliability and Safety (Inderscience Publishers Ltd)-Vol. 7, Iss: 4, pp 372-387
TL;DR: Experimental results show that the proposed model gives better fit to real failure data sets and predicts future failure behaviour of software development accurately than established models.
Abstract: In this paper, we propose a non-homogeneous Poisson process (NHPP) based S-shaped software reliability growth model (SRGM) in presence of imperfect debugging with a new exponentially increasing fault content function and S-shaped fault detection rate. We develop the fault content function considering learning capability of testing team during software development process. Fault content increases rapidly at the beginning of testing process while it grows gradually at the end of testing process due to increasing efficiency of testing team with testing time. We use maximum likelihood estimation (MLE) method to estimate model parameters. Applicability of the proposed model has been presented by comparing with established models in terms of goodness of fit and predictive validity using two software failure data sets. Experimental results show that the proposed model gives better fit to real failure data sets and predicts future failure behaviour of software development accurately than established models.
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TL;DR: A new model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP) and can give a significant improved goodness-of-fit and predictive performance is proposed.
90 citations
01 Sep 2014
TL;DR: Comparative studies demonstrate that the PFFNN DWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models.
Abstract: Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.
51 citations
TL;DR: A general model is used to derive models that incorporate the uncertainty of operating environments, which provides the flexibility in considering a different fault detection rate and random environmental factor and so on.
Abstract: This paper proposes a generalized model to cover imperfect debugging and the uncertainty of the operating environment and its effect on fault detection rate into software reliability evaluation based on a non-homogeneous Poisson process (NHPP). Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures over the past 40 years, but most of these models assume that the operating environment is the same as the testing environment. However, in fact, due to the unpredictability of the uncertain factors in the operating environments for the software, they may considerably influence the software’s reliability in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the original reliability prediction in the testing phase of the software development process, also from all its similar applications in other fields. In this paper, a general model is used to derive models that incorporate the uncertainty of operating environments, which provides the flexibility in considering a different fault detection rate and random environmental factor and so on. Several published models are shown to be covered by this general model and a new model is also developed and examined. The numerical illustrative examples of the proposed model have been validated on two sets of real software failure data in terms of six criteria. The comparison results demonstrate that the new model can fit and predict significantly better than other existing models.
38 citations
Cites background from "An S-shaped software reliability mo..."
...A constant h(t) means that failure intensity is proportional to the number of remaining faults, and an increasing h(t) means an increasing fault detection rate due to testing learning or an S-shaped h(t) attributed to fluctuations during the testing process [16], [17], or a combination of both mentioned above....
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TL;DR: Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.
Abstract: In this paper, we propose a non-homogeneous Poisson process (NHPP) based software reliability growth model (SRGM) in the presence of modified imperfect debugging and fault generation phenomenon. The testing team may not be able to remove a fault perfectly on observation of a failure due to the complexity of software systems and incomplete understanding of software, and the original fault may remain, or get replaced by another fault causing error generation. We have proposed an exponentially increasing fault content function and constant fault detection rate. The total fault content of the software for our proposed model increases rapidly at the beginning of the testing process. It grows gradually at the end of the testing process because of increasing efficiency of the testing team with testing time. We use the maximum likelihood estimation method to estimate the unknown parameters of the proposed model. The applicability of our proposed model and comparisons with established models in terms of goodness of fit and predictive validity have been presented using five known software failure data sets. Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.
33 citations
TL;DR: Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models.
Abstract: We propose ANN based logistic growth curve model (LGCM) of software reliability.We propose neuro-genetic approach for ANN based LGCM by optimizing ANN using GA.Proposed model is compared with NHPP and ANN based software reliability models.ANN based LGCM has better fitting and predictive capability than other models.If GA is applied to train ANN based LGCM, it will give upmost prediction accuracy. In this paper, we propose a multi-layer feedforward artificial neural network (ANN) based logistic growth curve model (LGCM) for software reliability estimation and prediction. We develop the ANN by designing different activation functions for the hidden layer neurons of the network. We explain the ANN from the mathematical viewpoint of logistic growth curve modeling for software reliability. We also propose a neuro-genetic approach for the ANN based LGCM by optimizing the weights of the network using proposed genetic algorithm (GA). We first train the ANN using back-propagation algorithm (BPA) to predict software reliability. After that, we use the proposed GA to train the ANN by globally optimizing the weights of the network. The proposed ANN based LGCM is compared with the traditional Non-homogeneous Poisson process (NHPP) based software reliability growth models (SRGMs) and ANN based software reliability models. We present the comparison between the two training algorithms when they are applied to train the proposed ANN to predict software reliability. The applicability of the different approaches is explained through three real software failure data sets. Experimental results demonstrate that the proposed ANN based LGCM has better fitting and predictive capability than the other NHPP and ANN based software reliability models. It is also noted that when the proposed GA is employed as the learning algorithm to the ANN, the proposed ANN based LGCM gives more fitting and prediction accuracy i.e. the proposed neuro-genetic approach to the LGCM provides utmost predictive validity. Proposed model can be applied during software testing time to get better software reliability estimation and prediction than the other traditional NHPP and ANN based software reliability models.
25 citations