International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us

IJSTR >> Volume 4 - Issue 10, October 2015 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

A Predicate Based Fault Localization Technique Based On Test Case Reduction

[Full Text]



Rohit Mishra, Dr.Raghav Yadav



Keywords: Fault Localization, Predicates, Dynamic Spectrum, Coincidental correctness, Class distribution, Coverage base matrix



ABSTRACT: In today’s world, software testing with statistical fault localization technique is one of most tedious, expensive and time consuming activity. In faulty program, a program element contrast dynamic spectra that estimate location of fault. There may have negative impact from coincidental correctness with these technique because in non failed run the fault can also be triggered out and if so, disturb the assessment of fault location. Now eliminating of confounding rules on the recognizing the accuracy. In this paper coincidental correctness which is an effective interface is the reason of success of fault location. We can find out fault predicates by distribution overlapping of dynamic spectrum in failed runs and non failed runs and slacken the area by referencing the inter class distances of spectra to clamp the less suspicious candidate. After that we apply coverage matrix base reduction approach to reduce the test cases of that program and locate the fault in that program. Finally, empirical result shows that our technique outshine with previous existing predicate based fault localization technique with test case reduction.



[1] P. Arumuga Nainar, T. Chen, J. Rosin, and B. Liblit, Statistical debugging using compound Boolean predicates, Proc. ISSTA ,pp. 5-15, 2007.

[2] Bhattacharyya. On a measure of divergence between two statistical populations defined by probability distributions. Bulletin of the Calcutta Mathematical Society,

[3] T.M. Chilimbi, B. Liblit, K. Mehra, A.V. Nori, and K. Vaswani, HOLMES: effective statistical debugging via efficient path profiling, Proc. ICSE, pp. 34-44, 2009.

[4] W. Dickinson, D. Leon, and A. Podgurski. Pursuing failure: the distribution of program failures in a profile space. Proc. ESEC/FSE, pp. 246-255, 2001.

[5] H. Do, S. G. Elbaum, and G. Rothermel, Supporting controlled experimentation with testing techniques: an infrastructure and its potential impact, Experimentation in Software Engineering, vol. 10(4), pp. 405-435, 2005.

[6] R. Gore, and P. F. Reynolds. Reducing confounding bias in predicate-level statistical debugging metrics, Proc. ICSE, pp. 463-473, 2012.

[7] Y. Guan, H. Wang. Set-valued information systems. Information Sciences, vol. 176(17), pp. 2507-2525, 2006.

[8] D. Hao, Y. Pan, L. Zhang, W. Zhao, H. Mei and J. Sun, A similarity-aware approach to testing based fault localization, Proc. ASE, pp. 291-294, 2005.

[9] J.A. Jones and M.J. Harrold, Empirical evaluation of the Tarantula automatic fault-localization technique, Proc. ASE, pp. 273-282, 2005.

[10] B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M.I. Jordan, Scalable statistical bug isolation, Proc. PLDI, pp. 15-26, 2005.

[11] C. Liu, L. Fei, X. Yan, S. P. Midkiff, and J. Han, Statistical debugging: a hypothesis testing-based approach, IEEE TSE, vol. 32(10), pp. 831-848, 2006.

[12] W. Masri and R. A. Assi. Cleansing test suites from coincidental correctness to enhance fault-localization, Proc. ICST, pp. 165-174, 2010.

[13] Y. Miao, Z. Chen, S. Li, Z. Zhao, and Y. Zhou, Identifying coincidental correctness for fault localization by clustering test cases, Proc. SEKE, pp. 262-272, 2012.

[14] L. Naish, H.J. Lee, and K. Ramamohanarao, A model for spectra-based software diagnosis, ACM TOSEM, vol. 20(3):11, 2011.

[15] R. Santelices, J.A. Jones, Y. Yu, and M.J. Harrold, Lightweight fault-localization using multiple coverage types, Proc. ICSE, pp. 56-66, 2009.

[16] S. Theodoridis, K. Koutroumbas. Pattern Recognition. Academic Press, New York, 4th. 2009.

[17] L. Wang, Feature selection with kernel class separability, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30(9), pp. 1534-1546, 2008.

[18] X. Wang, S. C. Cheung, W. K. Chan, and Z. Zhang. Taming coincidental correctness: Coverage refinement with context patterns to improve fault localization. Proc. ICSE, pp. 45-55, 2009.

[19] Y. Yu, J. A. Jones, and M.J. Harrold, An empirical study of the effects of test-suite reduction on fault localization, Proc. ICSE, pp. 201-210, 2008.

[20] Z. Zhang, W.K. Chan, T.H. Tse, B. Jiang, and X. Wang, Capturing propagation of infected program states, Proc. ESEC/FSE, pp.43-52, 2009 .

[21] Z. Zhang, B. Jiang, W. K. Chan, T. H. Tse, and X. Wang, Fault localization through evaluation sequences, Journal of Systems and Software, vol. 84(6), 2010.
[22] Heng Li, Yuzhen Liu (2014) “Program Structure Aware Fault LocalizationState Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences Beijing 100190, ChinaNorth China Electric PowerUniversity Beijing 100190, China

[23] Gong Dandan, WangTiantian ,SuXiaohong, MaPeijun (2014) “A test-suite reduction approach to improving fault-localization effectiveness” School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

[24] Heng Li, Yuzhen Liu, Zhenyu Zhang, Jian Liu (2014) “Program Structure Aware Fault Localization” State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences Beijing 100190, China

[25] Jifeng Xuan, Martin Monperrus (2014) “Test Case Purification for Improving Fault Localization” University of Lille & INRIA Lille, France