An Artificial Neural-Network-Based Approach to Software Reliability Assessment

Su, Yu Shen; Huang, Chin Yu; Chen, Yi Shin; Chen, Jing Xun

doi:10.1109/tencon.2005.301242

Cited by 20 publications

(11 citation statements)

References 17 publications

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“…Cai et al [137] have discussed the effectiveness of neural networks for handling dynamic software reliability data. Other noticeable works in this domain include Yu-Shen Su et al [138] , Hu et al [139], [140], Y Singh et al [148].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Soft Computing Techniques for Dependable Cyber-Physical Systems

et al. 2019

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Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Soft Computing Techniques for Dependable Cyber-Physical Systems

et al. 2019

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“…Boosting practices have been effectively used to enhance the functioning of other recognized processes from the Machine Learning field [12], such as ANN, and GP. Su et al [13] explained the neural network techniques in projects from mathematical point of view of software reliability modeling. Cai et al [14] trained the dataset using back propagation algorithm.…”

Section: 'Smentioning

confidence: 99%

Software Reliability Modeling: Comparative Analysis

Malhotra

2017

IJARCSSE

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-This paper includes the software reliability prediction and estimation as an area of software development process which needs to specify predict, estimate and asses the software reliability system. Software trustworthiness is an significant part of software quality along with functionality, performance, usability, maintainability, serviceability including many ones. Software reliability related to the software quality and directly affects the system reliability. There are hundreds of models developed from the last thirty to forty years to improve the software trustworthiness. The aim of this piece is to investigation of different software reliability models that can improve the trustworthiness of the software, their classification and finding the best fit for a particular project.

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“…Khoshgoftaar et al 9 used the neural network as a tool for predicting the number of faults in a program and concluded that the neural networks produce models with better quality of fit and predictive quality. Su et al 10,11 have proposed a neural network based approach to software reliability assessment combining various existing models into a Dynamic Weighted Combinational Model (DWCM). Kapur et al 12 have proposed a Generalized Dynamic Integrated Model (GDIM) using ANN approach, which incorporates the concept of n types of faults.…”

Section: Neural Network In Software Reliabilitymentioning

confidence: 99%

Software Reliability Assessment Using Artificial Neural Network Based Flexible Model Incorporating Faults of Different Complexity

Kapur

Khatri

Basirzadeh

2008

Int. J. Rel. Qual. Saf. Eng.

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With growth in demand for zero defects, predicting reliability of software products is gaining importance. Software Reliability Growth Models (SRGM) are used to estimate the reliability of a software product. We have a large number of SRGM; however none of them works across different environments. Recently, Artificial Neural Networks have been applied in software reliability assessment and software reliability growth prediction. In most of the existing research available in the literature, it is considered that similar testing effort is required on each debugging effort. However, in practice, different amount of testing efforts may be required for detection and removal of different type of faults on basis of their complexity. Consequently, faults are classified into three categories on basis of complexity: simple, hard and complex. In this paper we apply neural network methods to build software reliability growth models (SRGM) considering faults of different complexity. Logistic learning function accounting for the expertise gained by the testing team is used for modeling the proposed model. The proposed model assumes that in the simple faults the growth in removal process is uniform whereas, for hard and complex faults, removal process follows logistic growth curve due to the fact that learning of removal team grows as testing progresses. The proposed model has been validated, evaluated and compared with other NHPP model by applying it on two failure/fault removal data sets cited from real software development projects. The results show that 113 Int. J. Rel. Qual. Saf. Eng. 2008.15:113-127. Downloaded from www.worldscientific.com by GEORGE WASHINGTON UNIVERSITY on 02/07/15. For personal use only.the proposed model with logistic function provides improved goodness-of-fit for software failure/fault removal data.

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An Artificial Neural-Network-Based Approach to Software Reliability Assessment

Cited by 20 publications

References 17 publications

Soft Computing Techniques for Dependable Cyber-Physical Systems

Soft Computing Techniques for Dependable Cyber-Physical Systems

Software Reliability Modeling: Comparative Analysis

Software Reliability Assessment Using Artificial Neural Network Based Flexible Model Incorporating Faults of Different Complexity

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