基于源文件可疑度的软件缺陷定位方法研究
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摘要
提出了基于缺陷数据分析的软件缺陷定位方法,研究新的源文件可疑度概念及其计算方法,可疑度由缺陷数据和源文件的相关度、源文件的易错程度共同决定,可疑度越高,作为缺陷修复对象的概率越大。选用开源项目中的缺陷数据进行实验,将提出的定位方法与VSM、Bug Scout定位方法做对比,在Top N、MRR、MAP三项评估指标中有着较好的表现,实验结果说明了提出方法的有效性。
The software defect data localization method based on defect data analysis was proposed. A new concept of source file suspiciousness and its calculation were put forward,and the suspicious degree of this article was determined by the correlation between defect data and source file,the error degree of the source file. The higher the suspicious degree,and the greater the probability of the source file as the object to be repaired. Finally,it select the defect data in open source project to conduct an experiment. Compared with VSM and BugScout location methods,the proposed location method has a good performance among the three evaluation indexes of TopN,MRR and MAP. Experimental results demonstrate the effectiveness of the proposed method.
The software defect data localization method based on defect data analysis was proposed. A new concept of source file suspiciousness and its calculation were put forward,and the suspicious degree of this article was determined by the correlation between defect data and source file,the error degree of the source file. The higher the suspicious degree,and the greater the probability of the source file as the object to be repaired. Finally,it select the defect data in open source project to conduct an experiment. Compared with VSM and BugScout location methods,the proposed location method has a good performance among the three evaluation indexes of TopN,MRR and MAP. Experimental results demonstrate the effectiveness of the proposed method.
引文
[1]曹鹤玲,姜淑娟,鞠小林.软件错误定位研究综述[J].计算机科学,2014(2):1-6,14.
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[12]SAHA R K,LEASE M,KHURSHID S,et al.Improving bug localization using structured information retrieval[C]//Ieee/acm,International Conference on Automated Software Engineering.IEEE,2014:345-355.
[2]王克朝,王甜甜,苏小红,等.软件错误自动定位关键科学问题及研究进展[J].计算机学报,2015(11):2262-2278.
[3]SUVAJIT MANDAL,ARUP ABHINNA ACHARYA.A Novel Approach for Code-Base Fault Localization Technique[J].IOSR Journal of Computer Engineering.2014(1):35-38.
[4]王旭,张文,王青.基于缺陷修复历史的两阶段缺陷定位方法[J].计算机系统应用,2014(11):99-104.
[5]杨书新,刁文.一种改进的基于交叉矩阵统计的软件故障定位技术[J].计算机工程与科学,2016(4):679-685.
[6]孙媛,赵建军,周伟祝.航电控制计算机系统软件安全性框架研究[J].兵器装备工程学报,2018(3):135-142.
[7]杨淑莹,张桦.模式识别与智能计算-MATLAB技术实现[M].北京:电子工业出版社,2015.
[8]陈翔,顾庆,刘望舒,刘树龙,等.静态软件缺陷预测方法研究[J/OL].软件学报,2016,27(1):1-25.
[9]遗传优化支持向量机在软件缺陷预测中的应用[J].电子测量技术,2012,35(2):126-129.
[10]周志官,郭韵,李渴望.改进核函数的支持向量机智能诊断方法研究[J].轻工机械,2016,34(5):23-26.
[11]陈虎.基于特征抽取和SVM分类器的故障诊断[J].重庆工商大学学报(自然科学版),2016,33(5):25-28.
[12]SAHA R K,LEASE M,KHURSHID S,et al.Improving bug localization using structured information retrieval[C]//Ieee/acm,International Conference on Automated Software Engineering.IEEE,2014:345-355.