基于测试结果调整语句出错概率方法

J4 ›› 2014, Vol. 36 ›› Issue (05): 891-899.

基于测试结果调整语句出错概率方法

王蓁蓁

（1.金陵科技学院信息技术学院，江苏南京 211169;2.江苏省信息分析工程实验室，江苏南京 211169）

收稿日期:2013-01-29 修回日期:2013-04-03 出版日期:2014-05-25 发布日期:2014-05-25
基金资助:
国家自然科学基金资助项目（61170071）；金陵科技学院科研基金资助项目（jitn201305）

An approach of adjusting the probability
of error statements based on testing results

WANG Zhenzhen

(1.School of Information Technology,Jinling Institute of Technology,Nanjing 211169;
2.Information Analysis Engineering Laboratory of Jiangsu Province,Nanjing 211169,China)

Received:2013-01-29 Revised:2013-04-03 Online:2014-05-25 Published:2014-05-25

摘要/Abstract

摘要：

运用测试集对程序错误语句定位的算法被统称为TBFL方法。目前通行的TBFL算法一般都没有利用测试员、程序员关于测试用例和程序的先验知识，致使这些“资源”白白浪费。随机TBFL就是一类新型TBFL方法，其精神就是在随机理论的框架下，把这些先验知识和实际测试活动结合起来，从而对程序错误语句更好地定位。随机TBFL算法可以看成是这种类型算法的一般“模式”，人们可以从这个一般的模式里开发出不同的算法。基于测试结果调整语句出错概率的方法就是将随机TBFL算法中关于程序、测试集的先验知识和具体测试活动分离开来，根据测试结果再先后注入人们对测试集和程序的先验知识，从而更好地定位错误语句。在一些实例上，通过把新算法和随机TBFL算法进行对比，发现新算法是可取的。提出了三个有关不同TBFL算法比较标准，从这三个标准考察，新算法在上述实例上也是良好的。

关键词: 软件测试, 错误定位, 先验知识, 随机方法

Abstract:

Approaches for fault localization based on test suites are now collectively called TBFL (Testing Based Fault Localization). However, current algorithms do not take advantage of the prior knowledge about test cases and program so that they waste these valuable "resources". Stochastic TBFL is a new kind of TBFL approach whose spirit is to combine the prior knowledge with actual testing activities under stochastic theory, so as to locate program faults. Stochastic TBFL may be regarded as a general pattern of this approach, from which people can develop various algorithms. We separate the prior knowledge about program and test suites from actual testing activities, and according to testing results we then add the prior knowledge. This new method is called adjusting the probability of error statements based on testing results. Comparison to stochastic TBFL and some instances shows that our improved approach is feasible. Moreover, the paper proposes three standards for comparing different TBFL approaches. And from the investigation of the three standards, results of our proposed approach are also good.

Key words: software test;fault localization;prior knowledge;random method

王蓁蓁. 基于测试结果调整语句出错概率方法[J]. J4, 2014, 36(05): 891-899.

WANG Zhenzhen. An approach of adjusting the probability
of error statements based on testing results [J]. J4, 2014, 36(05): 891-899.

参考文献

［1］Agrawal H, Horgan J, London S, et al. Fault localization using execution slices and dataflow tests［C］∥Proc of the 6th International Symposium on Software Reliability Engineering, 1995:143151.
［2］Cleve H, Zeller A. Locating causes of program failures ［C］∥Proc of the 27th International Conference on Software Engineering, 2005:342351.
［3］Hao D, Zhang L, Pan Y, et al. On similarityawareness in testingbased fault localization ［J］. Automated Software Engineering, 2008, 15(2):207249.
［4］Kyriazis A, Mathioudakis K. Enhance of fault localization using probabilistic fusion with gas path analysis algorithms ［J］. Journal of Engineering for Gas Turbines and Power, 2009, 131(5):5160151609.
［5］Jones J A, Harrold M J, Stasko J. Visualization of test information to assist fault localization ［C］∥Proc of the 24th International Conference on Software Engineering, 2002:467477.
［6］Jones J A, Harrold M J. Empirical evaluation of the tarantula automatic faultlocalization technique ［C］∥Proc of the 20th IEEE/ACM International Conference on Automated Software Engineering, 2005:273282.
［7］Liblit B, Naik M, Zheng A X, et al. Scalable statistical bug isolation ［C］∥Proc of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI),2005:1516.
［8］Schach S R.Objectoriented classical software engineering ［M］. Beijing:China Machine Press, 2007.
［9］Liu C, Yan X, Fei L, et al. SOBER:Statistical modelbased bug localization ［C］∥Proc of the 13th ACM SIGSOFT Symposium on Foundations of Software Engineering, 2005:286295.
［10］Renieris M,Reiss S P.Fault localization with nearest neighbor queries ［C］∥Proc of the 18th International Conference on Automated Software Engineering, 2003:3039.
［11］Zeller A. Isolating causeeffect chains from computer programs ［C］∥Proc of the 10th ACM SIGFOFT Symposium on Foundations of Software Engineering, 2002:110.
［12］Wang Zhenzhen, Xu Baowen, Zhou Yuming, et al. New random testingbased fault localization approach ［J］. Computer Science, 2013, 40(1):514.(in Chinese)
［13］Wang Jianwu. Introduction to mathematical thinking method ［M］. Hefei:Anhui Education Press, 1996. (in Chinese)
［14］Wang Zhenzhen. Construction of knowledge database of nave fuzzy description logic and nave reasoning ［J］. Applied Science and Technology, 2012, 39(6):1829.(in Chinese)
附中文参考文献:
［12］王蓁蓁, 徐宝文, 周毓明, 等. 一种随机TBFL方法［J］. 计算机科学, 2013, 40(1):514.
［13］王健吾. 数学思维方法引论［M］.第1版.合肥:安徽教育出版社, 1996.
［14］王蓁蓁. 朴素模糊描述逻辑知识库构造及其朴素推理［J］. 应用科技, 2012, 39(6):1829.

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