Journal ArticleDOI
On the neural network approach in software reliability modeling
TLDR
The neural network approach is more appropriate for handling datasets with `smooth' trends than for Handling datasets with large fluctuations, and the training results are much better than the prediction results in general.About:
This article is published in Journal of Systems and Software.The article was published on 2001-08-01. It has received 152 citations till now. The article focuses on the topics: Network simulation & Network architecture.read more
Citations
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Journal ArticleDOI
Failure and reliability prediction by support vector machines regression of time series data
TL;DR: A comparative analysis of SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.
Journal Article
Software reliability forecasting by support vector machines with simulated annealing algorithms
Ping-Feng Pai,Wei-Chiang Hong +1 more
TL;DR: The experimental results reveal that the SVM model with simulated annealing algorithms (SVMSA) results in better predictions than the other methods, and the proposed model is a valid and promising alternative for forecasting software reliability.
Journal ArticleDOI
Evolutionary neural network modeling for software cumulative failure time prediction
Liang Tian,Afzel Noore +1 more
TL;DR: Numerical results show that both the goodness-of-fit and the next-step-predictability of the proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.
Journal ArticleDOI
Software reliability forecasting by support vector machines with simulated annealing algorithms
Ping-Feng Pai,Wei-Chiang Hong +1 more
TL;DR: In this article, simulated annealing algorithms (SA) are used to select the parameters of an SVM model and the experimental results reveal that the proposed model is a valid and promising alternative for forecasting software reliability.
Journal ArticleDOI
Neural-network-based approaches for software reliability estimation using dynamic weighted combinational models
Yu-Shen Su,Chin-Yu Huang +1 more
TL;DR: This paper explains the neural networks from the mathematical viewpoints of software reliability modeling and shows how to apply neural network to predict software reliability by designing different elements of neural networks and uses the approach to build a dynamic weighted combinational model (DWCM).
References
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Book
Software Reliability: Measurement, Prediction, Application
TL;DR: Revised and updated for professional software engineers, systems analysts and project managers, this book provides key concepts of software reliability and practical solutions for measuring reliability.
Journal ArticleDOI
Software Reliability Modeling
TL;DR: The use of stochastic techniques is justified, and the various probability models that have been proposed, along with any associated statistical estimation and inference procedures, are described.
Journal ArticleDOI
Using neural networks in reliability prediction
TL;DR: Results with actual testing and debugging data suggest that neural-network models are better at endpoint predictions than analytic models, and can be more accurate than some commonly used analytic models.
Journal ArticleDOI
Prediction of software reliability using connectionist models
TL;DR: The analysis shows that the connectionist approach is capable of developing models of varying complexity and may adapt well across different data sets and exhibit better predictive accuracy.
Journal ArticleDOI
Application of neural networks to software quality modeling of a very large telecommunications system
TL;DR: A case study of neural-network modeling techniques developed for the EMERALD system, which modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system and found the neural- network model had better predictive accuracy.
Related Papers (5)
Evolutionary neural network modeling for software cumulative failure time prediction
Liang Tian,Afzel Noore +1 more