Long-term software fault prediction with wavelet shrinkage estimation

Wu Jingchi

Dohi Tadashi

Okamura Hiroyuki

Journal of Systems and Software

Wavelet shrinkage estimation received considerable attentions to estimate stochastic processes such as a non-homogeneous Poisson process in a non-parametric way, and was applied to software reliability estimation/prediction. However, it lacks the prediction ability for unknown future patterns in long term and penalizes assessing the software reliability in practice. In this paper, we focus on the long-term prediction of the number of software faults detected in the testing phase and propose many novel long-term prediction methods based on the wavelet shrinkage estimation. The fundamental idea is to adopt both the denoised fault-count data and prediction values, and to minimize several kinds of loss functions to make effective predictions. We also develop an automated wavelet-based software reliability assessment tool, W-SRAT2, which is a drastic extension of the existing tool, W-SRAT, by adding those prediction algorithms. In numerical experiments with 6 actual software development project data, we investigate the predictive performance of our long-term prediction approaches, which consist of 2,640 combinations, and compare them with the common software reliability growth models with the maximum likelihood estimation. It is shown that our wavelet shrinkage estimation/prediction methods outperform the existing software reliability growth models.

Software reliability

Fault prediction

Non-homogeneous Poisson processes

Wavelet shrinkage estimation

Predictive performance

Tool development

eng

Elsevier

© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

This is not the published version. Please cite only the published version.

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[DOI] https://doi.org/10.1016/j.jss.2024.112123 isVersionOf