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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existence of software defects causes the software to fail to meet the needs of users. How to locate defects efficiently and with high quality is the key issue to eliminate software defects. Model-based defect location technology is a current research hot topic, and can be used to detect software system failures to find the cause of software failure. However, the existing model-based defect location technology didn’t consider the influence of transfer dependencies between non-adjacent nodes and different test cases on the suspicious degree, which led to the disadvantageous result of low positioning accuracy. This paper proposed a probabilistic model checking method for software fault location (PMC-SFL). Firstly, it proposed a program probability model to improve the reasoning ability of the model. Then it designed a learning algorithm based on the execution path to build a program probability model. Finally, it designed a software defect location algorithm based on probabilistic model detection for defect location analysis. Through experiments and analysis on the public dataset Siemens, it shows that the PMC-SFL method has higher software defect location accuracy and efficiency than five existing defect location methods RankCP, BNPDG, Tarantula, SOBER and CT. [ABSTRACT FROM AUTHOR]

Generally, it is assumed in the software reliability growth model that the fault is independent When a faut is detected, it can be eliminated. But in engineering, some detected fautts can not be eliminated and new faults may be introduced during the ellimination process. In this paper, a model based on a non-homogeneous Poisson process (NHPP) is proposed. It is assumed that the fault introduction process has a nonlinear ship with time. The software failure rate is based on the testing coverage. To avoid the restriction on Continuity and existence of model function derivative, an adaptive change real-valued genetic algorithm is applied to calculation in this paper. Finally, the model parameters are calculated through a set off real software failure data. The performance of the proposed model is compared with several existing NHPP sRGMs based in several criteria. The simplicity and accuracy in the proper model are discuss. [ABSTRACT FROM AUTHOR]