Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Roy, Pratik; Mahapatra, G. S.; Rani, Pooja; Pandey, Sudhir K.; Dey, Kashi Nath

doi:10.1016/j.asoc.2014.04.012

Cited by 52 publications

(28 citation statements)

References 24 publications

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“…That is why many researchers have focused on non‐PSRGMs. These models generally use different machine learning approaches such as neural networks and support vector machine . Neural network models have better robustness, but the convergence of the training process is slow and can easily fall into local minima, which cannot guarantee best solution.…”

Section: Related Workmentioning

confidence: 99%

An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

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Software reliability deals with the probability that software will not cause the failure of a system in a specified time interval. Software reliability growth models (SRGMs) are used to predict future behaviour from known characteristics of software, like historical failures. With the increasing demand to deliver quality software, more accurate SRGMs are required to estimate the software release time and cost of the testing effort. Software failure predictions at early phases also provide an opportunity for investing in proper quality assurance and upfront resource planning. Up till now, many parametric software reliability growth models (PSRGMs) have been proposed. However, several limitations of them mean that their predictive capacities differ from one dataset to others. In this paper, to enhance the prediction accuracy of existing PSRGMs, a high precision error iterative analysis method (HPEIAM) has been proposed based on the residual errors. In HPEIAM, residual errors from the estimated results of SRGMs are considered as another source of data that can combine the residual error modification with artificial neural network sign estimator. The repeated computation of residual errors by SRGMs improves and corrects the prediction accuracy up to the expected level. The performance of HPEIAM is tested with several PSRGMs using two sets of real software failure data based on three performance criteria. Moreover, we have compared the estimated failures predicted by HPEIAM with genetic algorithm (GA)-based prediction improvement. The results demonstrate that HPEIAM gives an improvement in goodness-of-fit and predictive performance for every PSRGM in initial few iterations.

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Section: Related Workmentioning

confidence: 99%

An improved software reliability prediction model by using high precision error iterative analysis method

Jabeen

Luo

Afzal

2019

Software Testing Verif & Rel

View full text Add to dashboard Cite

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“…Besides, the performance of dynamic neural networks is better than of static neural networks and dynamic networks can effectively extract the dynamic characteristics of systems [26]. Lately, RNNs have drawn much attention for getting dynamic features of systems [27], [28], [29], [30]. Because of their powerful characteristics, RNNs have been efficiently used for a wide range of problems such as time series predicting [31], [32], [33].…”

Section: Related Workmentioning

confidence: 99%

A Novel Method in Two-Step-Ahead Weight Adjustment of Recurrent Neural Networks: Application in Market Forecasting

Motlagh¹,

Ghiasi²

2016

ijacsa

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Abstract-Gold price prediction is a very complex nonlinear problem which is severely difficult. Real-time price prediction, as a principle of many economic models, is one of the most challenging tasks for economists since the context of the financial agents are often dynamic. Since in financial time series, direction prediction is important, in this work, an innovative Recurrent Neural Network (RNN) is utilized to obtain accurate Two-StepAhead (2SA) prediction results and ameliorate forecasting performances of gold market. The training method of the proposed network has been combined with an adaptive learning rate algorithm and a linear combination of Directional Symmetry (DS) is utilized in the training phase. The proposed method has been developed for online and offline applications. Simulations and experiments on the daily Gold market data and the benchmark time series of Lorenz and Rossler shows the high efficiency of proposed method which could forecast future gold price precisely.

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“…In [1], a robust ANN based on the median neuron model was proposed. Moreover, a robust, feed forward, and recurrent neural network model was presented in [17].…”

Section: Introductionmentioning

confidence: 99%

A New Multilayer Feedforward Network Based on Trimmed Mean Neuron Model

Yolcu¹,

Baş²,

Eğrioğlu³

et al. 2015

NNW

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The multilayer perceptron model has been suggested as an alternative to conventional approaches, and can accurately forecast time series. Additionally, several novel artificial neural network models have been proposed as alternatives to the multilayer perceptron model, which have used (for example) the generalizedmean, geometric mean, and multiplicative neuron models. Although all of these artificial neural network models can produce successful forecasts, their aggregation functions mean that they are negatively affected by outliers. In this study, we propose a new multilayer, feed forward neural network model, which is a robust model that uses the trimmed mean neuron model. Its aggregation function does not depend on outliers. We trained this multilayer, feed forward neural network using modified particle swarm optimization. We applied the proposed method to three well-known time series, and our results suggest that it produces superior forecasts when compared with similar methods.

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Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Cited by 52 publications

References 24 publications

An improved software reliability prediction model by using high precision error iterative analysis method

An improved software reliability prediction model by using high precision error iterative analysis method

A Novel Method in Two-Step-Ahead Weight Adjustment of Recurrent Neural Networks: Application in Market Forecasting

A New Multilayer Feedforward Network Based on Trimmed Mean Neuron Model

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