基于BBN的故障定位技术

柳永坡; 金茂忠; 贾晓霞; 刘雪梅

基于BBN的故障定位技术

1.
北京航空航天大学计算机学院, 北京 100191
2. 北京城市学院软件学部, 北京 100083

基金项目: 国家自然科学基金资助项目(60603039)

详细信息

作者简介:
柳永坡(1971-),男,辽宁辽阳人,博士生,liuypo@sei.buaa.edu.cn.

中图分类号: TP 311.5
计量
- 文章访问数: 3100
- HTML全文浏览量: 93
- PDF下载量: 992
- 被引次数: 0
出版历程
- 收稿日期: 2008-10-21
- 网络出版日期: 2009-10-31

BBN-based fault localization technique

1.
School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. College of Software, Beijing City University, Beijing 100083, China

摘要

摘要: 故障定位的目的是帮助程序员寻找引发失效的原因或故障位置,以加快调试过程.故障和失效间的关系往往非常复杂,难以直接描述故障到失效的转化.分析了采用差异分析的方法,提出基于可疑模式,构建故障推理贝叶斯网络,节点由可疑模式及其方法调用者构成;介绍了贝叶斯网络构建算法,各个相关概率的定义及BBN(Bayesian Belief Network)中各个边的条件概率计算公式.基于推理算法,得到包含故障的模块,并计算得到每个模块包含故障的概率.提出评价方法,并进行了实验验证,取得了平均0.761的定准率和0.737的定全率,定位结果良好有应用价值.
- 故障定位 /
- 差异 /
- 模式发现 /
- 概率
Abstract: Fault localization techniques help programmers find out the locations and the causes of the faults and accelerate the debugging process. The relation between the fault and the failure is usually complicated, making it hard to deduce how a fault causes the failure. At present, analysis of variance is broadly used in many recent correlative researches. A Bayesian belief network(BBN) for fault reasoning was constructed based on the suspicious pattern, whose nodes consist of the suspicious pattern and the callers of the methods that constitute the suspicious pattern. The constructing algorithm of the BBN, the correlative probabilities, and the formula for the conditional probabilities of each arc of the BBN were defined. A reasoning algorithm based on the BBN was proposed, through which the faulty module can be found and the probability for each module containing the fault can be calculated. An evaluation method was proposed. Experiments were executed to evaluation the fault localization technique. The data demonstrated that 0.761 in accuracy and 0.737 in recall on average were achieved by this technique. It is very effective in fault localization and has high practical value.
- fault location /
- differentiation /
- pattern recognition /
- probability

HTML全文

参考文献(1)

[1] Andreas Z, Ralf H. Simplifying and isolating failure-inducing input[J]. IEEE Transactions on Software Engineering, 2002, 28(2):183-200 [2] Mary J H, Gregg R, Kent S, et al. An empirical investigation of the relationship between spectra differences and regression faults [J]. Journal of Software Testing, Verification, and Reliability, 2000, 10(3):171-194 [3] Morell L J. A theory of fault-based testing[J]. IEEE Transactions on Software Engineering, 1990,16(8):844-857 [4] Voas J M. PIE: a dynamic failure-based technique[J]. IEEE Trans Software Eng, 1992, 18(8): 717-727 [5] James A J, Mary J H, John S. Visualization of test information to assist fault localization 24th International Conference on Software Engineering(ICSE 2002). Florida: ACM, 2002:467-477 [6] Shinji K, Akira N, Keisuke N, et al. Experimental evaluation of program slicing for fault localization[J]. Empirical Software Engineering, 2002 ,7(1):49-76 [7] Margaret A F. Fault localization through execution traces . USA: Georgia Institute of Technology, 2002 [8] Manos R, Steven P R. Fault localization with nearest neighbor queries 18th IEEE International Conference on Automated Software Engineering (ASE 2003). Montreal: IEEE Computer Society, 2003:30-39 [9] James A J, Mary J H. Empirical evaluation of the tarantula automatic fault-localization technique 20th IEEE/ACM International Conference on Automated Software Engineering (ASE 2005). California: ACM, 2005:273-282 [10] Wu Ji, Jia Xiaoxia, Liu Chang, et al. A statistical model to locate faults at input level IEEE 19th International Conference on Automated Software Engineering (ASE 2004). Linz: IEEE Computer Society, 2004:274-277 [11] 汪成亮,沈文武,张勤,等.以因果网为理论核心的网络故障诊断方法[J].计算机工程与应用,2004,40(4):151-153 Wang Chengliang, Shen Wenwu, Zhang Qin, et al. Computer network fault diagnosis based on causality diagram theory[J]. Computer Engineering and Applications,2004,40(4):151-153(in Chinese) [12] 李俭川.基于贝叶斯网络的智能故障诊断方法[J].中国惯性技术学报,2002,10(4):24-28 Li Jianchuan. Intelligent fault diagnosis technique based on bayesian networks [J]. Journal of Chinese Inertial Technology,2002,10(4):24-28(in Chinese) [13] 汪浩.基于数据挖掘技术的软件可靠性模型研究 .北京:北京航空航天大学计算机学院,2002 Wang Hao. Research of software reliability model based on data mining . Beijing: School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, 2002(in Chinese) [14] Murphy K P. A brief introduction to graphical models and bayesian Networks . http://www.ai.mit.edu/murphyk/Bayes/bayes.html [15] Valentin D, Christian L, Andreas Z. Lightweight defect localization for java The 19th European Conference on Object-Oriented Programming (ECOOP 2005). Glasgow, Scotland: Springer, 2005:528-550 [16] Valentin D, Christian L, Andrzej W, et al. Mining object behavior with ADABU The IEEE Fourth International Workshop on Dynamic Analysis(WODA 2006). Shanghai: ACM, 2006:17-24

施引文献

资源附件(0)

访问统计