Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Ten lectures on wavelets
Handbook of software reliability engineering
Handbook of software reliability engineering
Software Reliability
A non-parametric approach to software reliability: Research Articles
Applied Stochastic Models in Business and Industry - Reliability
Variational Bayesian Approach for Interval Estimation of NHPP-Based Software Reliability Models
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Nonparametric Analysis of the Order-Statistic Model in Software Reliability
IEEE Transactions on Software Engineering
Gompertz software reliability model: Estimation algorithm and empirical validation
Journal of Systems and Software
EM algorithm for discrete software reliability models: a unified parameter estimation method
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
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Recently, wavelet methods have been frequently used for not only multimedia information processing but also time series analysis with high speed and accuracy requirements. In this paper we apply the wavelet-based techniques to estimate software intensity functions in non-homogeneous Poisson process based software reliability models. There are two advantages for use of the wavelet-based estimation; (i) it is a non-parametric estimation without specifying a parametric form of the intensity function under any software debugging scenario, (ii) the computational overhead arising in statistical estimation is rather small. Especially, we apply two kinds of data transforms, called Anscombe transform and Fisz transform, and four kinds of thresholding schemes for empirical wavelet coefficients, to non-parametric estimation of software intensity functions. In numerical validation test with real software fault data, we show that our wavelet-based estimation method can provide higher goodness-of-fit performances than the conventional maximum likelihood estimation and the least squares estimation in some cases, in spite of its non-parametric nature.