Hongpo Wang, Ge Yang, Linnan Bai, Juan Yin, Qiang Li
Using software testing data collected, this paper established a software safety defects S curve model based on Weibull model theory. χ2 testing and prediction error testing are employed to verify the matching ability of the Weibull model and applicability of the predicting result. How to select truncation error is also discussed here. Results show that better predictive effect can be achieved if computational formula of truncation error is properly adjusted. The application of predicting model was also developed in this paper. Small sample fault data prediction and predicting error problems are discussed here. If the amount of fault data accumulated is not big enough, prediction cannot carry out. Analyzing results point out that it can be solved through the combination of different types of data. Then variation tendency of small sample fault data can be predicted.
{"title":"Small Sample Fault Data Prediction Study Based on Weibull Model","authors":"Hongpo Wang, Ge Yang, Linnan Bai, Juan Yin, Qiang Li","doi":"10.1109/CSMA.2015.9","DOIUrl":"https://doi.org/10.1109/CSMA.2015.9","url":null,"abstract":"Using software testing data collected, this paper established a software safety defects S curve model based on Weibull model theory. χ2 testing and prediction error testing are employed to verify the matching ability of the Weibull model and applicability of the predicting result. How to select truncation error is also discussed here. Results show that better predictive effect can be achieved if computational formula of truncation error is properly adjusted. The application of predicting model was also developed in this paper. Small sample fault data prediction and predicting error problems are discussed here. If the amount of fault data accumulated is not big enough, prediction cannot carry out. Analyzing results point out that it can be solved through the combination of different types of data. Then variation tendency of small sample fault data can be predicted.","PeriodicalId":205396,"journal":{"name":"2015 International Conference on Computer Science and Mechanical Automation (CSMA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128166325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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