[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

CGT-FL: using cooperative game theory to effective fault localization in presence of coincidental correctness

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

In this article we emphasize that most of the faults, appearing in real-world programs, are complicated and there exists a high interaction between faulty and other correlated statements, that is likely to cause coincidental correctness in many cases. To effectively diminish the negative impact of coincidentally correct tests on localization effectiveness, we suggest analyzing the combinatorial effect of program statements on the failure. To this end, we develop a new framework, CGT-FL, for evaluation and ranking program statements in a manner that statements which have strong discriminatory power as a group but are weak as individuals could be identified. The framework firstly evaluates the interactivity degree of each statement according to its influence on the intricate interrelation among statements by a Shapley value-based cooperative game-theoretic method. Then, statements are selected in a forward way by considering both interactivity and relevance measures. To verify the effectiveness of CGT-FL, we provide the results of our extensive experiments with different subject programs, containing seeded and real faults. The experimental results are then compared with those provided by different fault localization techniques for both single-fault and multiple-fault programs. The results prove the outperformance of CGT-FL compared to state-of-the-art techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://valgrind.org/

  2. http://www.elis.ugent.be/diablo/

  3. https://github.com/hammacher/javaslicer

  4. https://bitbucket.org/rjust/fault-localization-data/overview

  5. https://github.com/farid-feyzi/CGT-FL

References

  • Abreu R, Zoeteweij P, Golsteijn R, Van Gemund AJ (2009) A practical evaluation of spectrum-based fault localization. J Syst Softw 82(11):1780–1792

    Article  Google Scholar 

  • Androutsopoulos K, Clark D, Dan H, Hierons RM, Harman M (2014) An analysis of the relationship between conditional entropy and failed error propagation in software testing. In Proceedings of the 36th International Conference on Software Engineering. ACM, pp. 573–583

  • Baah GK, Podgurski A, Harrold MJ (2010) The probabilistic program dependence graph and its application to fault diagnosis. IEEE Trans Softw Eng 36(4):528–545

    Article  Google Scholar 

  • Burbea J, Rao C (1982) On the convexity of some divergence measures based on entropy functions. IEEE Trans Inf Theory 28(3):489–495

    Article  MathSciNet  Google Scholar 

  • Chekam TT, Papadakis M, Traon YL (2016) Assessing and comparing mutation-based fault localization techniques. arXiv preprint arXiv:1607.05512

  • Chen J, Li Q, Zhao J, Li X (2010) Test adequacy criterion based on coincidental correctness probability. In Proceedings of the Second Asia-Pacific Symposium on Internetware. ACM, p. 20

  • Cohen S, Dror G, Ruppin E (2007) Feature selection via coalitional game theory. Neural Comput 19(7):1939–1961

    Article  MathSciNet  Google Scholar 

  • Cover TM, Thomas JA (2012) Elements of information theory. Wiley, Hoboken

    MATH  Google Scholar 

  • Deng X, Papadimitriou CH (1994) On the complexity of cooperative solution concepts. Math Oper Res 19(2):257–266

    Article  MathSciNet  Google Scholar 

  • W. Dickinson, D. Leon, A. Podgurski (2001) Finding failures by cluster analysis of execution profiles. ICSE, pp. 339–348

  • DiGiuseppe N, Jones J (2012) Software behavior and failure clustering: An empirical study of fault causality. Proceedings of 5th International Conference on Software Testing, Verification and Validation (ICST), IEEE, pp. 191–200

  • Feyzi F, Parsa S (2017a) FPA-FL: incorporating static fault-proneness analysis into statistical fault localization. J Syst Softw. https://doi.org/10.1016/j.jss.2017.11.002

  • Feyzi F, Parsa S (2017b) Inforence: effective fault localization based on information-theoretic analysis and statistical causal inference. Front Comput Sci. https://doi.org/10.1007/s11704-017-6512-z

  • Feyzi F, Parsa S (2018a) Kernel-based detection of coincidentally correct test cases to improve fault localization effectiveness. Int J Appl Pattern Recog 5(2):119–136

    Article  Google Scholar 

  • Feyzi F, Parsa S (2018b) A program slicing-based method for effective detection of coincidentally correct test cases. Computing 100(9):927–969

    Article  MathSciNet  Google Scholar 

  • Feyzi F, Nikravan E, Parsa S (2016) FPA-Debug: Effective Statistical Fault Localization Considering Fault-proneness Analysis. arXiv preprint arXiv:1612.05780

  • Gupta N, He H, Zhang X, Gupta R (2005) Locating faulty code using failure-inducing chops. In Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering. ACM, pp. 263–272

  • Hierons RM (2006) Avoiding coincidental correctness in boundary value analysis. ACM Trans Softw Eng Methodol (TOSEM) 15(3):227–241

    Article  Google Scholar 

  • Hong S, Lee B, Kwak T, Jeon Y, Ko B, Kim Y, Kim M (2015) Mutation-based fault localization for real-world multilingual programs (T). In Automated Software Engineering (ASE), 2015 30th IEEE/ACM international conference on. IEEE, pp. 464–475

  • Jeffrey D, Gupta N, Gupta R (2008) Fault localization using value replacement. In Proceedings of the 2008 International Symposium on Software Testing and Analysis. ACM, pp. 167–178

  • Jones, J. A., & Harrold, M. J. (2005). Empirical evaluation of the tarantula automatic fault-localization technique. In Proceedings of the 20th IEEE/ACM international conference on automated software engineering. ACM (pp. 273–282)

  • Ju X, Jiang S, Chen X, Wang X, Zhang Y, Cao H (2014) HSFal: effective fault localization using hybrid spectrum of full slices and execution slices. J Syst Softw 90:3–17

    Article  Google Scholar 

  • Just R, Jalali D, Ernst MD (2014) Defects4J: a database of existing faults to enable controlled testing studies for Java programs. Proceedings of the 2014 International Symposium on Software Testing and Analysis, ACM, 437–440

  • Kochhar PS, Xia X, Lo D, Li S (2016) Practitioners’ expectations on automated fault localization. In proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 165–176

  • Le TDB, Thung F, Lo D (2013). Theory and practice, do they match? A case with spectrum-based fault localization. In Software Maintenance (ICSM), 2013 29th IEEE International Conference on. IEEE, pp. 380–383

  • Le TDB, Lo D, Li M (2015) Constrained feature selection for localizing faults. In software maintenance and evolution (ICSME), 2015 IEEE International Conference on. IEEE, pp. 501–505

  • T-DB Le, Lo, D., Le Goues, C., & Grunske, L. (2016) A learning-to-rank based fault localization approach using likely invariants. In Proceedings of the 25th International Symposium on Software Testing and Analysis. ACM, pp. 177–188

  • Li N, Li F, Offutt J (2012) Better algorithms to minimize the cost of test paths. Proceedings of 5th International Conference on Software Testing, Verification and Validation, IEEE, pp. 280–289

  • Lo D, Jiang L, Budi A (2010) Comprehensive evaluation of association measures for fault localization. In Software Maintenance (ICSM), 2010 IEEE International Conference on. IEEE, pp. 1–10

  • Masri, W., & Assi, R. A. (2010a, April). Cleansing test suites from coincidental correctness to enhance fault-localization. In 2010 third international conference on software testing, verification and validation (pp. 165-174). IEEE

  • Masri W, Assi RA (2010b). Cleansing test suites from coincidental correctness to enhance fault-localization. In 2010 Third International Conference on Software Testing, Verification and Validation. IEEE, pp. 165-174

  • Masri W, Assi RA (2014) Prevalence of coincidental correctness and mitigation of its impact on fault localization. ACM Trans Softw Eng Methodol (TOSEM) 23(1):8

    Article  Google Scholar 

  • Masri W, Podgurski A (2009) Measuring the strength of information flows in programs. ACM Trans Softw Eng Methodol (TOSEM) 19(2):5

    Article  Google Scholar 

  • Masri, W., Abou-Assi, R., El-Ghali, M., & Al-Fatairi, N. (2009). An empirical study of the factors that reduce the effectiveness of coverage-based fault localization. In Proceedings of the 2nd International Workshop on Defects in Large Software Systems: held in conjunction with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2009) (pp. 1–5). ACM

  • Masri W, Assi RA, Zaraket F, Fatairi N (2012) Enhancing fault localization via multivariate visualization. In Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on. IEEE, pp. 737–741

  • Meyer PE, Schretter C, Bontempi G (2008) Information-theoretic feature selection in microarray data using variable complementarity. IEEE J Sel Top Sig Process 2(3):261–274

    Article  Google Scholar 

  • Miao, Y., Chen, Z., Li, S., Zhao, Z., & Zhou, Y. (2012, June). Identifying coincidental correctness for fault localization by clustering test cases. In SEKE (pp. 267-272)

  • Miao Y, Chen Z, Li S, Zhao Z, Zhou Y (2013) A clustering-based strategy to identify coincidental correctness in fault localization. Int J Softw Eng Knowl Eng 23(05):721–741

    Article  Google Scholar 

  • Naish L, Lee HJ, Ramamohanarao K (2011) A model for spectra-based software diagnosis. ACM Trans Softw Eng Methodol (TOSEM) 20(3):11

    Article  Google Scholar 

  • Ott RL (1993) An introduction to statistical methods and data analysis, 4th edn. Duxbury Press, North Scituate

    Google Scholar 

  • Papadakis M, Le Traon Y (2015) Metallaxis-FL: mutation-based fault localization. Softw Test Verification Reliab 25(5–7):605–628

    Article  Google Scholar 

  • Pearson S, Campos J, Just R, Fraser G, Abreu R, Ernst MD, ... Keller B (2017) Evaluating and improving fault localization. In Software Engineering (ICSE), 2017 IEEE/ACM 39th International Conference on. IEEE, pp. 609–620

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  • Perez A, Abreu R, d’Amorim M (2017) Prevalence of single-fault fixes and its impact on fault localization. In 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST). IEEE, pp. 12–22

  • Roychowdhury S, Khurshid S (2011a) A novel framework for locating software faults using latent divergences. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, pp. 49–64

  • Roychowdhury S, Khurshid S (2011b) Software fault localization using feature selection. In Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering. ACM, pp. 11–18

  • Roychowdhury S, Khurshid S (2012) A family of generalized entropies and its application to software fault localization. In Intelligent Systems (IS), 2012 6th IEEE International Conference. IEEE, pp. 368-373

  • Shapley LS (1953) A value for n-person games. Contrib Theory Games 2(28):307–317

  • Sun X, Liu Y, Li J, Zhu J, Liu X, Chen H (2012) Using cooperative game theory to optimize the feature selection problem. Neurocomputing 97:86–93

    Article  Google Scholar 

  • Valizadegan H, Tan PN (2007) Kernel based detection of mislabeled training examples. In Proceedings of the 2007 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 309–319

  • Voas JM (1992) PIE: a dynamic failure-based technique. IEEE Trans Softw Eng 18(8):717–727

    Article  Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco

    MATH  Google Scholar 

  • Wong WE, Debroy V, Choi B (2010) A family of code coverage-based heuristics for effective fault localization. J Syst Softw 83(2):188–208

    Article  Google Scholar 

  • Wong WE, Debroy V, Xu D (2012) Towards better fault localization: a crosstab-based statistical approach. IEEE Trans Syst Man Cybern Part C Appl Rev 42(3):378–396

    Article  Google Scholar 

  • Wong WE, Debroy V, Gao R, Li Y (2014) The dstar method for effective software fault localization. IEEE Trans Reliab 63(1):290–308

    Article  Google Scholar 

  • Xie X, Kuo FC, Chen TY, Yoo S, Harman M (2013) Provably optimal and human-competitive results in sbse for spectrum based fault localisation. In international symposium on search based software engineering. Springer, Berlin pp. 224–238

  • Xu J, Zhang Z, Chan WK, Tse TH, Li S (2013) A general noise-reduction framework for fault localization of Java programs. Inf Softw Technol 55(5):880–896

    Article  Google Scholar 

  • Xue, X., Pang, Y., & Namin, A. S. (2014). Trimming test suites with coincidentally correct test cases for enhancing fault localizations. In computer software and applications conference (COMPSAC), 2014 IEEE 38th Annual. IEEE, pp. 239–244

  • Yan S, Chen Z, Zhao Z, Zhang C, Zhou Y (2010) A dynamic test cluster sampling strategy by leveraging execution spectra information. ICST, pp. 147–154

  • Yoo S (2012) Evolving human competitive spectra-based fault localisation techniques. In International Symposium on Search Based Software Engineering. Springer, Berlin, pp. 244–258

  • Yoo S, Xie X, Kuo FC, Chen TY, Harman M (2014) No pot of gold at the end of program spectrum rainbow: greatest risk evaluation formula does not exist. RN 14(14):14

    Google Scholar 

  • Zeller A, Hildebrandt R (2002) Simplifying and isolating failure-inducing input. IEEE Trans Softw Eng 28(2):183–200

    Article  Google Scholar 

  • Zhang X, Gupta R (2004) Whole execution traces. In 37th International Symposium on Microarchitecture (MICRO-37′04). IEEE, pp. 105–116

  • Zhang X, Gupta N, Gupta R (2006a). Pruning dynamic slices with confidence. In ACM SIGPLAN Notices 41(6): 169–180. ACM

  • Zhang X, Gupta N, Gupta R (2006b). Locating faults through automated predicate switching. In Proceedings of the 28th International Conference on Software Engineering. ACM, pp. 272–281

  • Zhang X, Tallam S, Gupta N, Gupta R (2007) Towards locating execution omission errors. In ACM Sigplan Notices 42(6): 415–424. ACM

  • Zhang M, Li X, Zhang L, Khurshid S (2017) Boosting spectrum-based fault localization using PageRank. In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, pp. 261–272

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Guilan, Rasht, Iran

    Farid Feyzi

Authors
  1. Farid Feyzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farid Feyzi.

Additional information

Guest Editor: Shin Yoo

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feyzi, F. CGT-FL: using cooperative game theory to effective fault localization in presence of coincidental correctness. Empir Software Eng 25, 3873–3927 (2020). https://doi.org/10.1007/s10664-020-09859-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10664-020-09859-y

Keywords

Search

Navigation