Research Article
Prediction Model Based Failure Time Data for Software Reliability
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@INPROCEEDINGS{10.1007/978-3-319-73317-3_32, author={Peng Lin and Xu Tian and Xiaojuan Wang and Xu Cao and Jiejing Cao and Jianli Li and Yan Gong}, title={Prediction Model Based Failure Time Data for Software Reliability}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Defect prediction Poisson process Data fitting Software reliability}, doi={10.1007/978-3-319-73317-3_32} }- Peng Lin
Xu Tian
Xiaojuan Wang
Xu Cao
Jiejing Cao
Jianli Li
Yan Gong
Year: 2018
Prediction Model Based Failure Time Data for Software Reliability
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_32
Abstract
Since all the defects cannot be detected within a finite software testing process (STP), the failure data should be wisely used to estimate the potential defects for software reliability. Therefore, a standard graphical methodology (GM) model is proposed for software reliability, in which failure data of time domain is utilized to predict the potential defects. First, non homogeneous and compound Poisson process is involved to model the failure time during STP. Then, GM model is utilized to predict the potential defects. Further, the software reliability is estimated based on GM model. Finally, compared with the traditional models, GM model can reach an improvement of 30% relative gain on average.
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