基于关联分类的FPGA缺陷检测水平量化方法

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英文篇名：Quantization method of FPGA defect detection level based on associative classification 作者：王志昊 ; 冯志华 英文作者：WANG Zhi-hao;FENG Zhi-hua;Institute 706,Second Academy of China Aerospace Science and Industry Corporation; 关键词：现场可编程门阵列 ; 缺陷检测 ; 缺陷分类 ; 关联规则 ; 马尔科夫链 英文关键词：FPGA;;defect detection;;defect classification;;association rule;;Markov chain 中文刊名：SJSJ 英文刊名：Computer Engineering and Design 机构：中国航天科工集团第二研究院706所; 出版日期：2019-07-16 出版单位：计算机工程与设计 年：2019 期：v.40;No.391 语种：中文; 页：SJSJ201907012 页数：6 CN：07 ISSN：11-1775/TP 分类号：80-85

摘要

为客观、全面地量化FPGA缺陷检测水平,并以此实现FPGA测试方检测水平比对,在统一的缺陷分类标准基础上,对面向FPGA缺陷的关联分类技术进行研究,借此建立不同项目间统一的水平量化标准,结合马尔科夫链理论,研究针对检测水平变化趋势的分析方法,给出稳定水平量化方法。结合实例,阐述应用该方法的一般步骤,结果验证了该方法在FPGA缺陷检测水平的量化与对比工作上的可行性与有效性。
        To objectively and comprehensively quantify the level of FPGA defect detection,and to achieve comparison of detection level,the associative classification technology for FPGA defects was studied based on the unified defect classification standard.The unified standard of level quantification among different projects was established.Combining with Markov chain theory,the analysis method for the trend of detection level was studied,and the stable level quantification method was provided.The general steps of applying the method were illustrated using an example.It is verified that the method is feasible and effective in the quantification and comparison of FPGA defect detection level.

引文

[1]YOU Yi,DUAN Hongdong,WANG Hanchao.Quality metrics and analysis system based on defect density method of software testing[J].Computer Simulation,2015,32(8):451-454(in Chinese).[尤艺,段洪栋,王瀚超.缺陷密度法的软件测试质量度量分析技术[J].计算机仿真,2015,32(8):451-454.]
    [2]FEI Qingchun,SHI Yingying,WANG Weiwei.An evaluation method for software testing quality based on grey theory[J].Measurement&Control Technology,2016,35(3):110-112(in Chinese).[费清春,史莹莹,王卫蔚.基于灰色理论的软件测试质量评价方法[J].测控技术,2016,35(3):110-112.]
    [3]WANG Yongli,WANG Yanjun,ZHANG Haijun.The weight and evaluation model for software testing ability comparison among software testing laboratories[J].Research and Exploration in Laboratory,2015,34(3):246-250(in Chinese).[王勇利,王艳军,张海军.基于加权估计的软件实验室能力比对测试结果评价[J].实验室研究与探索,2015,34(3):246-250.]
    [4]FENG Zhihua,LI Dongfang,CHEN Lirong.Research on FP-GA software defect classification[C]//Proc of the National Rugged Computer,2016(in Chinese).[冯志华,李东方,陈丽容.FPGA软件缺陷分类研究[C]//全国抗恶劣环境计算机学术年会,2016.]
    [5]Abdelhamid N,Thabtah F.Associative classification approaches:Review and comparison[J].Journal of Information&Knowledge Management,2014,13(3):1450027.
    [6]Czibula G,Marian Z,Czibula IG.Software defect prediction using relational association rule mining[J].Information Sciences,2014,264(183):260-278.
    [7]Han WJ,Jiang HY,Lu TB,et al.Software defect model based on similarity and association rule[J].International Journal of Multimedia&Ubiquitous Engineering,2015,10(7):1-10.
    [8]YAN Yueming.Research of software defect analysis based on association rules mining[J].Computer Engineering&Software,2017,38(1):70-76(in Chinese).[颜乐鸣.基于关联规则挖掘的软件缺陷分析研究[J].软件,2017,38(1):70-76.]
    [9]YI Jin,LUO Xun,AO Jianxun,et al.Software fault classification prediction model based on Markov chain[J].Journal of University of Chinese Academy of Sciences,2013,30(4):562-567(in Chinese).[易锦,罗峋,凹建勋,等.基于马尔科夫链的软件故障分类预测模型[J].中国科学院大学学报,2013,30(4):562-567.]
    [10]CHENG Heng,QU Xiaomei.Markov chain in the application of the teaching evaluation[J].Journal of Hubei Normal University(Natural Science),2015,35(1):75-78(in Chinese).[程恒,屈小妹.马尔科夫链在教学评价中的应用[J].湖北师范学院学报(自然科学版),2015,35(1):75-78.]