Junruo Sun, Huancheng Su, Haifeng Li, Chang Liu, Jiabin Chen, Xi Liu
{"title":"基于考虑领域特征的改进潜狄利克雷分配主题模型的软件缺陷分类方法","authors":"Junruo Sun, Huancheng Su, Haifeng Li, Chang Liu, Jiabin Chen, Xi Liu","doi":"10.1109/DSA51864.2020.00081","DOIUrl":null,"url":null,"abstract":"The existing defect classification approaches do not consider the domain characters of software defects, such as aeronautics domain, astronautics domain and so on. Therefore, the precision rate and the recall rate of these defect classification approaches are not very accurate in many conditions. To resolve this problem4 we present a new defect classification approach based on the modified Latent Dirichlet Allocation (LDA for short) topic model combining with domain characters to improve the performance of the defect classification. First, we propose the defect segmentation approach based on the special domain characters. Then, we propose a modified LDA topic model combining with software requirements. Based on the proposed modified LDA model, we obtain a new defect classification approach. Finally, the experiment result shows that the precision rate and the recall rate of the defect classification are au improved up to 15%~20% compared with the existing classification models. Thus, we consider that this new classification approach is very suitable for classifying the defects with obvious domain characters.","PeriodicalId":436097,"journal":{"name":"International Conferences on Dependable Systems and Their Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Software Defect Classification Approach Based on the Modified Latent Dirichlet Allocation Topic Model Considering the Domain Characters\",\"authors\":\"Junruo Sun, Huancheng Su, Haifeng Li, Chang Liu, Jiabin Chen, Xi Liu\",\"doi\":\"10.1109/DSA51864.2020.00081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The existing defect classification approaches do not consider the domain characters of software defects, such as aeronautics domain, astronautics domain and so on. Therefore, the precision rate and the recall rate of these defect classification approaches are not very accurate in many conditions. To resolve this problem4 we present a new defect classification approach based on the modified Latent Dirichlet Allocation (LDA for short) topic model combining with domain characters to improve the performance of the defect classification. First, we propose the defect segmentation approach based on the special domain characters. Then, we propose a modified LDA topic model combining with software requirements. Based on the proposed modified LDA model, we obtain a new defect classification approach. Finally, the experiment result shows that the precision rate and the recall rate of the defect classification are au improved up to 15%~20% compared with the existing classification models. Thus, we consider that this new classification approach is very suitable for classifying the defects with obvious domain characters.\",\"PeriodicalId\":436097,\"journal\":{\"name\":\"International Conferences on Dependable Systems and Their Applications\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conferences on Dependable Systems and Their Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSA51864.2020.00081\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conferences on Dependable Systems and Their Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSA51864.2020.00081","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Software Defect Classification Approach Based on the Modified Latent Dirichlet Allocation Topic Model Considering the Domain Characters
The existing defect classification approaches do not consider the domain characters of software defects, such as aeronautics domain, astronautics domain and so on. Therefore, the precision rate and the recall rate of these defect classification approaches are not very accurate in many conditions. To resolve this problem4 we present a new defect classification approach based on the modified Latent Dirichlet Allocation (LDA for short) topic model combining with domain characters to improve the performance of the defect classification. First, we propose the defect segmentation approach based on the special domain characters. Then, we propose a modified LDA topic model combining with software requirements. Based on the proposed modified LDA model, we obtain a new defect classification approach. Finally, the experiment result shows that the precision rate and the recall rate of the defect classification are au improved up to 15%~20% compared with the existing classification models. Thus, we consider that this new classification approach is very suitable for classifying the defects with obvious domain characters.