Pub Date : 1900-01-01DOI: 10.1109/QRS-C.2018.00025
N. Zhang, Haiyan Chai, Xinyu Han
Based on the current situation about warship equipment software that there are more development workload, less testing efficiency and test workload, this paper introduces the test case reuse technology of warship equipment software to solve this problem effectively. First of all, history test cases of warship equipment software are analyzed and the general pattern of test cases is extracted. Then the subject words tree of test case will be set up to describe the history test case set regularly. At last, the new test cases based on the regular description and history test cases will both be searched using keywords and semantic to realize the test case reuse.
{"title":"Research on the Reuse of Test Case for Warship Equipment Software","authors":"N. Zhang, Haiyan Chai, Xinyu Han","doi":"10.1109/QRS-C.2018.00025","DOIUrl":"https://doi.org/10.1109/QRS-C.2018.00025","url":null,"abstract":"Based on the current situation about warship equipment software that there are more development workload, less testing efficiency and test workload, this paper introduces the test case reuse technology of warship equipment software to solve this problem effectively. First of all, history test cases of warship equipment software are analyzed and the general pattern of test cases is extracted. Then the subject words tree of test case will be set up to describe the history test case set regularly. At last, the new test cases based on the regular description and history test cases will both be searched using keywords and semantic to realize the test case reuse.","PeriodicalId":426575,"journal":{"name":"QRS Companion","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124993969","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}
In Recent years, sparse representation has been significantly applied in many image processing problems, such as image restoration, face recognition and image super-resolution, and shown promising results. The key issue of sparse representation is how to find a reasonable representation dictionary, through which the image can be presented more sparsely. In this paper, we addressed the biological image restoration, which was an important preprocessing technique for security identification, and proposed a novel sparse-based cost function. Considering the significant difference of underlying structure within different patches, we independently trained the dictionary using a set of self-similarity patches to present each patch more sparsely. To solve the proposed cost function, an approach based on alternating optimization was presented to obtain the approximate solution. Some experiments on face and palm images demonstrated that the proposed method was superior to many existing excellent methods.
{"title":"A Biological Image Restoration Method with Independently Local Dictionary Learning","authors":"Qidi Wu, Yibing Li","doi":"10.1109/QRS-C.2017.48","DOIUrl":"https://doi.org/10.1109/QRS-C.2017.48","url":null,"abstract":"In Recent years, sparse representation has been significantly applied in many image processing problems, such as image restoration, face recognition and image super-resolution, and shown promising results. The key issue of sparse representation is how to find a reasonable representation dictionary, through which the image can be presented more sparsely. In this paper, we addressed the biological image restoration, which was an important preprocessing technique for security identification, and proposed a novel sparse-based cost function. Considering the significant difference of underlying structure within different patches, we independently trained the dictionary using a set of self-similarity patches to present each patch more sparsely. To solve the proposed cost function, an approach based on alternating optimization was presented to obtain the approximate solution. Some experiments on face and palm images demonstrated that the proposed method was superior to many existing excellent methods.","PeriodicalId":426575,"journal":{"name":"QRS Companion","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169952","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}
Pub Date : 1900-01-01DOI: 10.1109/QRS-C51114.2020.00017
Xi Liu, Haifeng Li, Xuyang Xie
Radar software defects are not used and applied effectively and sufficiently in the testing process. As a result, defects often occur repeatedly, which causes safety hazards in software operation. To resolve this problem, this paper proposed a novel intelligent defect prediction approach for radar software by using Naïve Bayesian to classify defect data and predict defects according to radar software requirements. We apply the proposed approach on the typical radar software. The experiment results show that the defect prediction precision rate of the proposed defect prediction approach is 75%, and the prediction recall rate is 70%, approx. The experiment results are better compared to the defect prediction methods without Naïve Bayesian and defect classification. Therefore, the proposed defect prediction approach can be applied on radar software effectively and applicably to improve the effectiveness of radar software testing and provide the positive feedback to the radar software design process significantly.
{"title":"Intelligent Radar Software Defect Prediction Approach and Its Application","authors":"Xi Liu, Haifeng Li, Xuyang Xie","doi":"10.1109/QRS-C51114.2020.00017","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00017","url":null,"abstract":"Radar software defects are not used and applied effectively and sufficiently in the testing process. As a result, defects often occur repeatedly, which causes safety hazards in software operation. To resolve this problem, this paper proposed a novel intelligent defect prediction approach for radar software by using Naïve Bayesian to classify defect data and predict defects according to radar software requirements. We apply the proposed approach on the typical radar software. The experiment results show that the defect prediction precision rate of the proposed defect prediction approach is 75%, and the prediction recall rate is 70%, approx. The experiment results are better compared to the defect prediction methods without Naïve Bayesian and defect classification. Therefore, the proposed defect prediction approach can be applied on radar software effectively and applicably to improve the effectiveness of radar software testing and provide the positive feedback to the radar software design process significantly.","PeriodicalId":426575,"journal":{"name":"QRS Companion","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116450701","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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