IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra

Xiaobing SUN, Xin PENG, Bin LI, Bixin LI, Wanzhi WEN

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Front. Comput. Sci. ›› 2016, Vol. 10 ›› Issue (5) : 812-831. DOI: 10.1007/s11704-016-5226-y
RESEARCH ARTICLE

IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra

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Abstract

Fault localization is an important and challenging task during software testing. Among techniques studied in this field, program spectrum based fault localization is a promising approach. To perform spectrum based fault localization, a set of test oracles should be provided, and the effectiveness of fault localization depends highly on the quality of test oracles. Moreover, their effectiveness is usually affected when multiple simultaneous faults are present. Faced with multiple faults it is difficult for developers to determine when to stop the fault localization process. To address these issues, we propose an iterative fault localization process, i.e., an iterative process of selecting test cases for effective fault localization (IPSETFUL), to identify as many faults as possible in the program until the stopping criterion is satisfied. It is performed based on a concept lattice of program spectrum (CLPS) proposed in our previous work. Based on the labeling approach of CLPS, program statements are categorized as dangerous statements, safe statements, and sensitive statements. To identify the faults, developers need to check the dangerous statements. Meantime, developers need to select a set of test cases covering the dangerous or sensitive statements from the original test suite, and a new CLPS is generated for the next iteration. The same process is proceeded in the same way. This iterative process ends until there are no failing tests in the test suite and all statements on the CLPS become safe statements. We conduct an empirical study on several subject programs, and the results show that IPSETFUL can help identifymost of the faults in the program with the given test suite. Moreover, it can save much effort in inspecting unfaulty program statements compared with the existing spectrum based fault localization techniques and the relevant state of the art technique.

Keywords

fault localization / program spectrum / concept lattice / test case selection / iterative process

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Xiaobing SUN, Xin PENG, Bin LI, Bixin LI, Wanzhi WEN. IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra. Front. Comput. Sci., 2016, 10(5): 812‒831 https://doi.org/10.1007/s11704-016-5226-y

References

[1]
Jones J A, Harrold M J. Empirical evaluation of the tarantula automatic fault-localization technique. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering. 2005, 273–282
CrossRef Google scholar
[2]
Le T D B, Lo D. Will fault localization work for these failures? An automated approach to predict effectiveness of fault localization tools. In: Proceedings of the 2013 IEEE International Conference on Software Maintenance. 2013, 310–319
CrossRef Google scholar
[3]
Jeffrey D, Gupta N, Gupta R. Effective and efficient localization of multiple faults using value replacement. In: Proceedings of IEEE International Conference on Software Maintenance. 2009, 221–230
CrossRef Google scholar
[4]
Nainar P A, Liblit B. Adaptive bug isolation. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. 2010, 255–264
[5]
Xuan J F, Monperrus M. Test case purification for improving fault localization. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. 2014, 52–63
CrossRef Google scholar
[6]
Papadakis M, Le Traon Y. Effective fault localization viamutation analysis: a selective mutation approach. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing. 2014, 1293–1300
[7]
Mao X G, Lei Y, Dai Z Y, Qi Y H, Wang C S. Slice-based statistical fault localization. Journal of Systems and Software, 2014, 89: 51–62
CrossRef Google scholar
[8]
Lucia, Lo D, Xia X. Fusion fault localizers. In: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering. 2014, 127–138
[9]
Ju X, Jiang S, Chen X, Wang X Y, Zhang Y M, Cao H L. HSFal: effective fault localization using hybrid spectrum of full slices and execution slices. Journal of Systems and Software, 2014, 90: 3–17
CrossRef Google scholar
[10]
Xie X Y, Chen T Y, Kuo F C, Xu B W. A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization. ACM Transations on Software Engineering and Methodology, 2013, 22(4)
[11]
Yoo S, Harman M, Clark D. Fault localization prioritization: comparing information-theoretic and coverage-based approaches. ACM Transations on Software Engineering and Methodology, 2013, 22(3)
[12]
Orso A, Rothermel G. Software testing: a research travelogue (2000– 2014). In: Proceedings of the on Future of Software Engineering. 2014, 117–132
CrossRef Google scholar
[13]
Parnin C, Orso A. Are automated debugging techniques actually helping programmers? In: Proceedings of the 20th International Symposium on Software Testing and Analysis. 2011, 199–209
CrossRef Google scholar
[14]
Zheng A X, Jordan M I, Liblit B, Naik M, Aiken A. Statistical debugging: simultaneous identification of multiple bugs. In: Proceedings of the 23rd International Conference on Machine Learning. 2006, 1105–1112
CrossRef Google scholar
[15]
Abreu R, Zoeteweij P, van Gemund A J C. Spectrum-based multiple fault localization. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering. 2009, 88–99
CrossRef Google scholar
[16]
Cellier P, Ducassé M, Ferré S, Ridoux O. Multiple fault localization with data mining. In: Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE’2011). 2011, 238–243
[17]
Perez A, Abreu R, Riboira A. A dynamic code coverage approach to maximize fault localization efficiency. Journal of Systems and Software, 2014, 90: 18–28
CrossRef Google scholar
[18]
Moon S, Kim Y, Kim M, Yoo S. Ask the mutants: mutating faulty programs for fault localization. In: Proceedings of the 2014 IEEE International Conference on Software Testing, Verification, and Validation. 2014, 153–162
CrossRef Google scholar
[19]
Artzi S, Dolby J, Tip F, Pistoia M. Directed test generation for effective fault localization. In: Proceedings of the 19th International Symposium on Software Testing and Analysis. 2010, 49–60
CrossRef Google scholar
[20]
Sun X B, Li B X, Wen W Z. CLPS-MFL: using concept lattice of program spectrum for effective multi-fault localization. In: Proceedings of the 13th International Conference on Quality Software. 2013, 204–207
CrossRef Google scholar
[21]
Liu C, Yan X F, Fei L, Han J W, Midkiff S P. SOBER: statistical modelbased bug localization. In: Proceedings of the 10th European Software Engineering Conference Held Jointly with the 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering. 2005, 286–295
CrossRef Google scholar
[22]
Jones J A, Harrold M J, Stasko J. Visualization of test information to assist fault localization. In: Proceedings of the 24th International Conference on Software Engineering. 2002, 467–477
CrossRef Google scholar
[23]
Abreu R, Zoeteweij P, van Gemund A J. On the accuracy of spectrumbased fault localization. In: Proceedings of Testing: Academic and Industrial Conference Practice and Research Techniques-MUTATION, 2007. 2007, 89–98
[24]
Ganter B, Wille R. Formal Concept Analysis: Mathematical Foundations. Berlin: Springer-Verlag, 1986
[25]
Tilley T, Cole R, Becker P, Eklund P. A survey of formal concept analysis support for software engineering activities. Formal Concept Analysis, 2005, 250–271
CrossRef Google scholar
[26]
Sun X B, Chen Y, Li B, Li B X. Exploring software engineering data with formal concept analysis. In: Proceedings of 2013 International workshop on Data Analysis Patterns in Software Engineering. 2013, 14–16
CrossRef Google scholar
[27]
Poshyvanyk D, Gethers M, Marcus A. Concept location using formal concept analysis and information retrieval. ACM Transations on Software Engineering and Methodology, 2012, 21(4): 23
CrossRef Google scholar
[28]
Birkhoff G. Lattice Theory. Providence: American Mathematical Society Colloquium Publications, 1940
CrossRef Google scholar
[29]
Cigarrĺćn J M, Gonzalo J, Peñas A, Verdejo F. Browsing search results via formal concept analysis: automatic selection of attributes. In: Proceedings of International Conference on Formal Concept Analysis. 2004, 74–87
CrossRef Google scholar
[30]
van der Merwe D, Obiedkov S, Kourie D. AddIntent: a new incremental algorithm for constructing concept lattices. In: Proceedings of International Conference on Formal Concept Analysis. 2004, 372–385
CrossRef Google scholar
[31]
Santelices R, Jones J A, Yu Y B, Harrold M J. Lightweight faultlocalization using multiple coverage types. In: Proceedings of the 31st International Conference on Software Engineering. 2009, 56–66
[32]
Chen M Y, Kiciman E, Fratkin E, Fox A, Brewer E. Pinpoint: problem determination in large, dynamic internet services. In: Proceedings of the 32nd IEEE/IFIP International Conference on Dependable Systems and Networks. 2002, 595–604
CrossRef Google scholar
[33]
Steimann F, Frenkel M. Improving coverage-based localization of multiple faults using algorithms from integer linear programming. In: Proceedings of the 23rd IEEE International Symposium on Software Reliability Engineering. 2012, 121–130
CrossRef Google scholar
[34]
Weiser M. Program slicing. IEEE Transactions on Software Engineering, 1984, 10(4): 352–357
CrossRef Google scholar
[35]
DiGiuseppe N, Jones J A. On the influence of multiple faults on coverage-based fault localization. In: Proceedings of the 2011 International Symposium on Software Testing and Analysis. 2011, 210–220
CrossRef Google scholar
[36]
Xie X Y, Wong W E, Chen T Y, Xu B W. Metamorphic slice: an application in spectrum-based fault localization. Information of Software Technology, 2013, 55(5): 866–879
CrossRef Google scholar
[37]
Debroy V, Wong W E. Combining mutation and fault localization for automated program debugging. Journal of Systems and Software, 2014, 90: 45–60
CrossRef Google scholar
[38]
Xuan J F, Monperrus M. Learning to combine multiple ranking metrics for fault localization. In: Proceedings of the 30th IEEE International Conference on Software Maintenance and Evolution. 2014, 191–200
CrossRef Google scholar
[39]
Gong P, Zhao R L, Li Z. Faster mutation-based fault localization with a novel mutation execution strategy. In: Proceedings of the 8th IEEE International Conference on Software Testing, Verification and Validation. 2015, 1–10
CrossRef Google scholar
[40]
Zhang X Y, Gupta R. Cost effective dynamic program slicing. ACM SIGPLAN Notice, 2004, 39(6): 94–106
CrossRef Google scholar
[41]
Chen T Y, Cheung Y Y. Dynamic program dicing. In: Proceedings of the Conference on Software Maintenance. 1993, 378–385
CrossRef Google scholar
[42]
Wong W E, Qi Y. Effective program debugging based on execution slices and inter-block data dependency. Journal of Systems and Software, 2006, 79(7): 891–903
CrossRef Google scholar
[43]
Gyimothy T, Beszedes Á, Forgacs I. An efficient relevant slicing method for debugging. In: Proceedings of the Conference on Foundations of Software Engineering. 1999, 303–321
CrossRef Google scholar
[44]
Baah G K, Podgurski A, Harrold M J. Causal inference for statistical fault localization. In: Proceedings of the 19th International Symposium on Software Testing and Analysis. 2010, 73–84
CrossRef Google scholar
[45]
Liblit B, Naik M, Zheng A X, Aiken A, Jordan M I. Scalable statistical bug isolation. In: Proceedings of the 2005 ACMSIGPLAN Conference on Programming Language Design and Implementation. 2005, 15–26
CrossRef Google scholar
[46]
Jones J A, Bowring J F, Harrold M J. Debugging in parallel. In: Proceedings of the 2007 International Symposium on Software Testing and Analysis. 2007, 16–26
CrossRef Google scholar
[47]
Hogerle W, Steimann F, Frenkel M. More debugging in parallel. In: Proceedings of the 25th IEEE International Symposium on Software Reliability Engineering, ISSRE 2014. 2014, 133–143
CrossRef Google scholar
[48]
Gong D D, Su X H, Wang T T, Ma P J, Yua W. State dependency probabilistic model for fault localization. Information of Software Technology, 2015, 57(1): 430–445
CrossRef Google scholar
[49]
Jiang B, Zhai K, Chan W K, Tse T H, Zhang Z Y. On the adoption of MC/DC and control-flow adequacy for a tight integration of program testing and statistical fault localization. Information of Software Technology, 2013, 55(5): 897–917
CrossRef Google scholar
[50]
Gong D D, Wang T T, Sa X H, Ma P S. A test-suite reduction approach to improving fault-localization effectiveness. Computer Language, Systems & Structure, 2013, 39(3): 95–108
CrossRef Google scholar

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