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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.

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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

    Chapter  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

    Chapter  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

    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

    Google Scholar 

  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

    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

    Google Scholar 

  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

    Article  Google Scholar 

  8. Lucia D, Lo D, Xia X. Fusion fault localizers. In: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering. 2014, 127–138

    Google Scholar 

  9. Ju X, Jiang S, Chen X, Wang XY, Zhang YM, 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

    Article  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

    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

    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

    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

    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

    Google Scholar 

  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

    Article  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

    Chapter  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

    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

    Google Scholar 

  21. Liu C, Yan X F, Fei L, Han JW, 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

    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

    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

    Google Scholar 

  24. Ganter B, Wille R. Formal Concept Analysis: Mathematical Foundations. Berlin: Springer-Verlag, 1986

    MATH  Google Scholar 

  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

    Chapter  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

    Chapter  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

    Google Scholar 

  28. Birkhoff G. Lattice Theory. Providence: American Mathematical Society Colloquium Publications, 1940

    Book  MATH  Google Scholar 

  29. Cigarrlcn 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

    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

    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

    Google Scholar 

  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

    Chapter  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

    Google Scholar 

  34. Weiser M. Program slicing. IEEE Transactions on Software Engineering, 1984, 10(4): 352–357

    Article  MATH  Google Scholar 

  35. Di Giuseppe 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

    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

    Article  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

    Article  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

    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

    Google Scholar 

  40. Zhang X Y, Gupta R. Cost effective dynamic program slicing. ACM SIGPLAN Notice, 2004, 39(6): 94–106

    Article  Google Scholar 

  41. Chen T Y, Cheung Y Y. Dynamic program dicing. In: Proceedings of the Conference on Software Maintenance. 1993, 378–385

    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

    Article  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

    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

    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

    Chapter  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

    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

    Chapter  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

    Article  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

    Article  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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, 200433, China

    Xiaobing Sun & Xin Peng

  2. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, 200433, China

    Xiaobing Sun & Xin Peng

  3. School of Information Engineering, Yangzhou University, Yangzhou, 225127, China

    Xiaobing Sun & Bin Li

  4. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Bixin Li

  5. School of Computer Science and Technology, Nantong University, Nantong, 226019, China

    Wanzhi Wen

Authors
  1. Xiaobing Sun
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  2. Xin Peng
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  3. Bin Li
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  4. Bixin Li
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  5. Wanzhi Wen
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Corresponding author

Correspondence to Xiaobing Sun.

Additional information

Xiaobing Sun is an associate professor in School of Information Engineering at Yangzhou University, China. He is now conducting his post-doctor in the Software School of Fudan University, China. He is a CCF and ACM member. His current research interests include software analysis and testing, and software data analytics.

Xin Peng is a professor in the Software School of Fudan University, China. He is a senior CCF member and ACM member. His current research interests include software maintenance and reengineering, software reuse, software product line, and adaptive software systems.

Bin Li is a professor in School of Information Engineering at Yangzhou University, China. He is a senior CCF member and ACM member. His current research interests includeWeb service analysis and cloud computing, artificial intelligence, machine learning, and crowdsourcing computing.

Bixin Li is a professor in School of Computer Science and Engineering at Southeast University, China. He is a senior CCF member. His research interests include program analysis, and software modeling, analysis, maintenance, testing, and verification.

Wanzhi Wen is a lecture in School of Computer Science and Technology at Nantong University, China. He is a CCF member. His current research interests include software analysis and testing, software fault localization.

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Sun, X., Peng, X., Li, B. et al. IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra. Front. Comput. Sci. 10, 812–831 (2016). https://doi.org/10.1007/s11704-016-5226-y

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