Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 88-92.doi: 10.11896/jsjkx.210200096

• Intelligent Computing • Previous Articles     Next Articles

Multiple Fault Localization Method Based on Deep Convolutional Network

ZHANG Hui   

  1. School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHANG Hui,born in 1982,Ph.D,lec-turer.Her main research interests include fault localization and software testing.

Abstract: Most of the current fault localization methods solve single fault localization,but the faults are related to each other.How to find the relationship between these faults and the test results and the relationship between the faults,and reduce the impact of coincidental correct test cases and similar test cases on the suspiciousness of sentences is very important to improve the efficiency of multiple fault localization.In order to solve the above problems,this paper proposes a multiple fault localization method based on deep convolutional network.A set of suspiciousness with high accuracy is obtained through a deep convolutional network with a special structure,and then applied to forward slicing and backward slicing,the correlation between faults and faults is found to locate multiple faults.Experiments show that the multiple fault localization efficiency of the method in this paper is stronger than that of the existing classic fault localization methods.

Key words: Deep convolutional network, Fault localization, Slicing

CLC Number: 

  • TP311.5
[1]WEISER M.Program Slicing[J].IEEE Transactions on Software Engineering,1984,SE-10(4):352-357.
[2]JU X,JIANG S,XIANG C,et al.HSFal:Effective fault localization using hybrid spectrum of full slices and execution slices[J].Journal of Systems & Software,2014,90(APR.):3-17.
[3]KOREL B.PELAS-program error-locating assistant system[J].IEEE Transactions on Software Engineering,1988,14(9):1253-1260.
[4]LIAN L,KUSUMOTO S,KIKUNO T,et al.A new fault localizing method for the program debugging process[J].Information &Software Technology,1997,39(4):271-284.
[5]MAO X,YAN L,DAI Z,et al.Slice-based statistical fault localization[J].Journal of Systems & Software,2014,89(MAR.):51-62.
[6]BAAH G K,PODGURSKI A,HARROLD M J.The Probabilistic Program Dependence Graph and Its Application to FaultDiag-nosis[J].IEEE Transactions on Software Engineering,2009,36(4):528-545.
[7]ZHANG Z,CHARS W K,TSETH,et al.Capturingpropagation of infected program states[C]//Proceedings of the7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering(ESEC/FSE'09).New York:ACM,2009:43-52.
[8]ZELLER A,HILDEBRANDT R.Simplifying and Isolating Fai-lure-Inducing Input[J].IEEE Transactions on Software Engineering,2002,28(2):183-200.
[9]CHENG H,LO D,ZHOU Y,et al.Identifying bug signaturesusing discriminative graph mining[C]//Proceedings of the Eighteenth International Symposium on Software Testing and Ana-lysis.Chicago:ACM,2009:141-152.
[10]NIU X T,NIE C H,LEI Y,et al.Identifying failure-inducingcombinations using tuple relationship[C]//2013 IEEE Sixth International Conference on Software Testing,Verification and Validation Workshops.Luxembourg:IEEE,2013:271-280.
[11]JONESJ A,HARROLDM J.Empirical evaluation of the tarantula automatic fault-localization technique[C]//Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering.Long Beach CA:ACM,2005:273-282.
[12]CLEVE H,ZELLER A.Locating causes of program failures[C]//27th International Conference on Software Engineering.St.Louis,MO:IEEE,2005:342-351.
[13]HARROLD M J,ROTHERMEL G,SAYRE K,et al.An empir-ical investigation of the relationship between spectra differences and regression faults[J].Software Testing,Verification and Reliability,2000,10(3):171-194.
[14]RENIERES M,REISS S P.Fault localization with nearestneighbor queries[C]//18th IEEE International Conference on Automated Software Engineering.Montreal,QC:IEEE,2003:30-39.
[15]WONG W E,DEBROY V,CHOI B.A family of code coverage-based heuristics for effective fault localization[J].Journal of Systems & Software,2010,83(2):188-208.
[16]WONG W E,DEBROY V,LI Y,et al.Software fault localization using dstar (d*)[C]//2012 IEEE Sixth International Confe-rence on Software Security and Reliability.Gaithersburg,MD:IEEE,2012:21-30.
[17]WONG W E,DEBROY V,GAO R,et al.The DStar Method for Effective Software Fault Localization[J].IEEE Transactions on Reliability,2014,63(1):290-308.
[18]WEN W Z,LI B X,SUN X B,et al.Multiple error location based on conditional execution slice spectrum[J].Computer Research and Development,2013,50(5):1030-1043.
[19]CAO H L,JIANG S J.Multiple error location method based on Chameleon cluster analysis[J].Acta Electronica Sinica,2017,45(2):394-400.
[20]WANG X Y,JIANG S J,GAO P F,et al.A software multiple defect location method based on fuzzy C-means clustering[J].Chinese Journal of Computers,2020,43(2):206-232.
[21]WANG Z,FAN X Y,ZOU Y G,et al.A Multiple Defect Location Method Based on Genetic Algorithm[J].Journal of Software,2016,27(4):879-900.
[22]WONG W E,QI Y.BP Neural Network-Based Effective aultLocalization[J].International Journal of Software Engineering and Knowledge Engineering,2011,19(4):573-597.
[23]WONG W E,SHI Y,QI Y,et al.Using an RBF neural network to locate program bugs[C]//2008 19th International Sympo-sium on Software Reliability Engineering (ISSRE).Seattle,WA:IEEE,2008:27-36.
[24]DEBROYV,WONG W,XU X F,et al.A grouping-based strategy to improve the effectiveness of fault localization techniques[C]//2010 10th International Conference on Quality Software.IEEE,2010:13-22.
[25]LAM A N,NGUYEN A T,NGUYEN H A,et al.Bug localization with combination of deep learning and information retrieval[C]//2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).Buenos Aires:IEEE,2017:218-229.
[26]ZHANG Z,LEI Y,TAN Q,et al.Deep Learning-Based Fault Localization with Contextual Information[J].IEICE Transactions on Information and Systems,2017,E100.D(12):3027-3031.
[27]ZHENG W,HU D,WANG J.Fault Localization Analysis Based on Deep Neural Network[J].Mathematical Problems in Engineering,2016,2016(pt.4):1-11.
[28]ENISER H F,GERASIMOU S,SEN A.Deepfault:Fault localization for deep neural networks[C]//International Conference on Fundamental Approaches to Software Engineering.Prague:Springer,2019:171-191.
[29]MARU A,DUTTA A,KUMAR K V,et al.Effective Software Fault Localization Using a Back Propagation Neural Network[C]//Computational Intelligence in Data Mining.Singapore:Springer,2020:513-526.
[30]HERIS S R,KEYVANPOUR M R.Effectiveness of Weighted Neural Network on Accuracy of Software Fault Localization[C]//2019 5th International Conference on Web Research (ICWR).Tehran:IEEE,2019:100-104.
[31]LIU P.Artificial Intelligence[M].Beijing:China Water Power Press,2021:92-98.
[32]LI B X.Technology and Application of Program Slicing[M].Bei-jing:Science Press,2006:7.
[33]ABREU R,ZOETEWEIJ P,VAN GEMUND A J C.On the accuracy of spectrum-based fault localization[C]//Testing:Academic and Industrial Conference Practice and Research Techniques-MUTATION (TAICPART-MUTATION 2007).Windsor:IEEE,2007:89-98.
[34]ABREU R,ZOETEWEIJ P,VAN GEMUND A J C.An evaluation of similarity coefficients for software fault localization[C]//2006 12th Pacific Rim International Symposium on Dependable Computing (PRDĆ06).Riverside,CA:IEEE,2006:39-46.
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