An Efficient Particle Swarm Optimization-Based Neural Network Approach for Software Reliability Assessment
19 Apr 2017-International Journal of Reliability, Quality and Safety Engineering (World Scientific Publishing Company)-Vol. 24, Iss: 04, pp 1750019
TL;DR: An artificial neural network (ANN)-based dynamic weighted combination model trained by novel particle swarm optimization (PSO) algorithm is proposed for software reliability prediction and different software reliability growth models (SRGMs) are merged based on the weights derived by the learning algorithm.
Abstract: In this paper, an artificial neural network (ANN)-based dynamic weighted combination model trained by novel particle swarm optimization (PSO) algorithm is proposed for software reliability prediction. Different software reliability growth models (SRGMs) are merged based on the weights derived by the learning algorithm of the proposed ANN. To avoid trapping in local minima during training of the ANN, we propose a neighborhood-based adaptive PSO (NAPSO) algorithm for learning of the proposed ANN in order to find global optimal weights. We conduct the experiments on real software failure data sets for validation of the proposed dynamic weighted combination model (PDWCM). Fitting performance and predictability of the proposed PSO-based neural network are compared with the conventional PSO-based neural network (CPSO) and existing ANN-based software reliability models. We also compare the performance of the proposed PSO algorithm with the CPSO algorithm through learning of the proposed ANN. Empirical results in...
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TL;DR: In this article, an artificial neural network and #x0028 ANN ) based software reliability model trained by novel particle swarm optimization (PSO) algorithm was proposed for enhanced forecasting of the reliability of software.
Abstract: This paper proposes an artificial neural network ( ANN ) based software reliability model trained by novel particle swarm optimization ( PSO ) algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.
49 citations
Cites methods from "An Efficient Particle Swarm Optimiz..."
...Arar and Ayan [36] proposed a classification approach using traditional ANN and novel artificial bee colony algorithm in software defect prediction problem....
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Proceedings Article•
01 Jan 2007TL;DR: This paper proposes a hierarchical mixture of software reliability models (HMSRM) for software reliability prediction, an application of the hierarchical mixtures of experts (HME) architecture that can automatically select the most appropriate lower-level model for the data and performances are well in prediction.
Abstract: It is important to develop general prediction models in current software reliability research. In this paper, we propose a hierarchical mixture of software reliability models (HMSRM) for software reliability prediction. This is an application of the hierarchical mixtures of experts (HME) architecture. In HMSRM, individual software reliability models are used as experts. During the training of HMSRM, an Expectation-Maximizing (EM) algorithm is employed to estimate the parameters of the model. Experiments illustrate that our approach performs quite well in the later stages of software development, and better than single classical software reliability models. We show that the method can automatically select the most appropriate lower-level model for the data and performances are well in prediction.
24 citations
TL;DR: The study revealed that the maximum system availability level of 99.9845% is obtained and assisted the decision-makers in planning the maintenance activity as per the criticality level of subsystems for allocating the resources.
18 citations
TL;DR: A prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability and has more accurate prediction results compared with BP network, support vector machine, long short term memory networks (LSTM), and autoregressive model (AR).
17 citations
TL;DR: To enhance the reliability of SRGM, the parameters ofSRGM estimated using TEF and enhanced using chaotic maps to improve search performance are enhanced and the constrained benchmark functions the results of chaotic maps are obtained.
Abstract: Software reliability growth model (SRGM) with modified testing-effort function (TEF) is a function to evaluate and foresee the parameters of the data. Reliability of software is portrayed as the distinct possibility that for a predefined time, a software package will continue to run on an advance domain without frustration. SRGM utilized a few optimization procedure algorithms to advance the parameters by bifurcating them into a few stages however to upgrade the technique by using all of the parameters at the same time, the algorithm utilized is the chaotic grey wolf optimization algorithm (CGWO). CGWO is an advanced heuristic system for portraying the execution by achieving complex parameter optimization and designing application issues. Different parametric reliabilities rely upon the attributes or characteristics of the data. The parameters are predicted using the Pham–Zhang (PZ) model. Tandem computer software dataset DS1 and DS2 are used to compare the predicted parameter of SRGM obtained by Pham–Zhang (PZ) model using testing effort functions (TEFs) based on the evaluation metrics mean square error (MSE), relative error (RE) and coefficient of determination (R2). To enhance the reliability of SRGM, the parameters of SRGM estimated using TEF and enhanced using chaotic maps to improve search performance. By using the constrained benchmark functions the results of chaotic maps are obtained. Based on the chaotic graph results, the Chebyshev graph shows a good convergence rate of 78%. Overall, 86% of the results revealed an association between the choice variable and fitness criteria for CGWO. In the SRGM using CGWO, the expected result is completely mechanized and does not require any client necessity.
16 citations
References
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TL;DR: The failure process is analyzed to develop a suitable meanvalue function for the NHPP to create a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process.
Abstract: This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP). The failure process is analyzed to develop a suitable meanvalue function for the NHPP; expressions are given for several performance measures. Actual software failure data are analyzed and compared with a previous analysis.
1,704 citations
TL;DR: In this article, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm, also referred to as PSO-BP algorithm, is proposed to train the weights of feedforward neural network (FNN), the hybrid algorithm can make use of not only strong global searching ability of the PSOA, but also strong local searching capability of the BP algorithm.
591 citations
TL;DR: This paper aims to offer a compendious and timely review of the field and the challenges and opportunities offered by this welcome addition to the optimization toolbox.
Abstract: Particle Swarm Optimization (PSO), in its present form, has been in existence for roughly a decade, with formative research in related domains (such as social modelling, computer graphics, simulation and animation of natural swarms or flocks) for some years before that; a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridisation and specialisation, and demonstration of some interesting emergent behaviour. This paper aims to offer a compendious and timely review of the field and the challenges and opportunities offered by this welcome addition to the optimization toolbox. Part I discusses the location of PSO within the broader domain of natural computing, considers the development of the algorithm, and refinements introduced to prevent swarm stagnation and tackle dynamic environments. Part II considers current research in hybridisation, combinatorial problems, multicriteria and constrained optimization, and a range of indicative application areas.
585 citations
TL;DR: A hybrid PSO algorithm is proposed, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities.
366 citations
TL;DR: A viable method for the software quality assessment, which integrates the capture-recapture method and the models above, is discussed, and its application to actual test data is illustrated.
Abstract: The s-shaped growth curves of detected software errors can be observed in software testing. The delayed s-shaped and inflection s-shaped software reliability growth models based on a nonhomogeneous Poisson process are discussed. The software reliability growth types of the models are investigated in terms of the error detection rate per error. In addition, a viable method for the software quality assessment, which integrates the capture-recapture method and the models above, is discussed, and its application to actual test data is illustrated.
277 citations