Failure and reliability prediction by support vector machines regression of time series data

Robust recurrent neural network modeling for software fault detection and correction prediction

Software reliability prediction by soft computing techniques

Software reliability is an important factor for quantitatively characterizing software quality and estimating the duration of software testing period. Traditional parametric software reliability growth models (SRGMs) such as nonhomogeneous Poisson process (NHPP) models have been successfully utilized in practical software reliability engineering. However, no single such parametric model can obtain accurate prediction for all cases. In addition to the parametric models, non-parametric models like neural network have shown to be effective alternative techniques for software reliability prediction. In this paper, we propose a non-parametric software reliability prediction system based on neural network ensembles. The effects of system architecture on the performance are investigated. The comparative studies between the proposed system with the single neural network based system and three parametric NHPP models are carried out. The experimental results demonstrate that the system predictability can be significantly improved by combing multiple neural networks.

Predicting software reliability with neural network ensembles

On-line prediction of software reliability using an evolutionary connectionist model

When analysing software failure data, many software reliability models are available and in particular, nonhomogeneous Poisson process (NHPP) models are commonly used. However, difficulties posed by the assumptions, their validity, and relevance of these assumptions to the real testing environment have limited their usefulness. The connectionist approach using neural network models are more flexible and with less restrictive assumptions. This model-free technique requires only the failure history as inputs and then develops its own internal model of failure process. Their ability to model nonlinear patterns and learn from the data makes it a valuable alternative methodology for characterising the failure process. In this paper, a modified Elman recurrent neural network in modeling and predicting software failures is investigated. The effects of different feedback weights in the proposed model are also studied. A comparative study between the proposed recurrent architecture, with the more popular feedforward neural network, the Jordan recurrent model, and some traditional parametric software reliability growth models are carried out.

Siu Lau Ho

Papers

A study of the connectionist models for software reliability prediction