Skip to main content
SpringerLink
Log in

Model-Based Software Debugging

  • Chapter
  • First Online:
Fault Diagnosis of Dynamic Systems

  • 980 Accesses

Abstract

The complexity and size of software systems have rapidly increased in recent years, with software engineers facing ever-growing challenges in building and maintaining such systems. In particular, testing and debugging, that is, finding, isolating, and eliminating defects in software systems still constitute a major challenge in practice [47].

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The Year 2000 problem is also known as Y2K problem, Y2K bug, or simply Y2K.

  2. 2.

    As said in the previous section, by component we mean the unit by which we gather coverage. Basically, components are the columns in the hit-spectra matrix and can represent, e.g., every statement in the source code.

  3. 3.

    A candidate d is said to be minimal if no valid candidate \(d'\) is contained in d.

  4. 4.

    Probabilities are calculated assuming conditional independence throughout the process.

  5. 5.

    In the context of development-time fault localization, we often approximate \(p_j\) as 1/1000, i.e., 1 fault for each 1000 lines of code.

  6. 6.

    For an efficient implementation of Staccato, refer to https://github.com/npcardoso/MHS2 or http://mhs2.algorun.org/.

  7. 7.

    Simulator is available at https://github.com/SERG-Delft/sfl-simulator.

References

  1. Abreu, R., Mayer, W., Stumptner, M., van Gemund, A.J.: Refining spectrum-based fault localization rankings. In: Proceedings of the 2009 ACM symposium on Applied Computing, pp. 409–414. ACM (2009)

    Google Scholar 

  2. Abreu, R., Van Gemund, A.J.: A low-cost approximate minimal hitting set algorithm and its application to model-based diagnosis. SARA 9, 2–9 (2009)

    Google Scholar 

  3. Abreu, R., Zoeteweij, P., Van Gemund, A.J.: An observation-based model for fault localization. In: Proceedings of the 2008 International Workshop on Dynamic Analysis: Held in Conjunction with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2008), pp. 64–70. ACM (2008)

    Google Scholar 

  4. Abreu, R., Zoeteweij, P., Van Gemund, A.J.: Spectrum-based multiple fault localization. In: Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering, pp. 88–99. IEEE Computer Society (2009)

    Google Scholar 

  5. Abreu, R., Zoeteweij, P., van Gemund, A.J.C.: On the accuracy of spectrum-based fault localization. In: TAICPART-MUTATION ’07: Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION, pp. 89–98. IEEE Computer Society, Washington, DC, USA (2007)

    Google Scholar 

  6. Abreu, R., Zoeteweij, P., Van Gemund, A.J.C.: A new bayesian approach to multiple intermittent fault diagnosis. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI’09, pp. 653–658 (2009)

    Google Scholar 

  7. Araki, K., Furukawa, Z., Cheng, J.: A general framework for debugging. IEEE Softw. 8(3), 14–20 (1991)

    Article  Google Scholar 

  8. Avižienis, A., Laprie, J.C., Randell, B., Landwehr, C.E.: Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Dependable Secur. Comput. 1(1), 11–33 (2004)

    Article  Google Scholar 

  9. Avriel, M.: Nonlinear Programming: Analysis and Methods. Courier Corporation, North Chelmsford (2003)

    MATH  Google Scholar 

  10. Batory, D.: Feature models, grammars, and propositional formulas. In: Proceedings of the 9th international conference on Software Product Lines, SPLC’05, pp. 7–20. Springer, Berlin (2005)

    Chapter  Google Scholar 

  11. Benavides, D., Segura, S., Ruiz-Cortés, A.: Automated analysis of feature models 20 years later: a literature review. Inf. Syst. 35(6), 615–636 (2010)

    Article  Google Scholar 

  12. Benavides, D., Trinidad, P., Ruiz-Cortés, A.: Automated reasoning on feature models. In: LNCS, Advanced Information Systems Engineering: 17th International Conference, CAISE 2005, p. 2005. Springer (2005)

    Google Scholar 

  13. Brat, G., Drusinsky, D., Giannakopoulou, D., Goldberg, A., Havelund, K., Lowry, M., Pasareanu, C., Venet, A., Visser, W., Washington, R.: Experimental evaluation of verification and validation tools on martian rover software. Form. Methods Syst. Des. 25(2–3), 167–198 (2004)

    Article  Google Scholar 

  14. Briand, L.C., Labiche, Y., Sun, H.: Investigating the use of analysis contracts to support fault isolation in object oriented code. In: Proceedings of the International Symposium on Software Testing and Analysis, ISSTA 2002, Roma, Italy, 22–24 July 2002, pp. 70–80 (2002)

    Google Scholar 

  15. Campos, J., Abreu, R., Fraser, G., d’Amorim, M.: Entropy-based test generation for improved fault localization. In: Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering, pp. 257–267. IEEE Press (2013)

    Google Scholar 

  16. Campos, J., Arcuri, A., Fraser, G., Abreu, R.: Continuous test generation: enhancing continuous integration with automated test generation. In: Proceedings of the 29th ACM/IEEE international conference on Automated Software Engineering, pp. 55–66. ACM (2014)

    Google Scholar 

  17. Campos, J., Riboira, A., Perez, A., Abreu, R.: Gzoltar: an eclipse plug-in for testing and debugging. In: Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering, pp. 378–381. ACM (2012)

    Google Scholar 

  18. Cardoso, N., Abreu, R.: A distributed approach to diagnosis candidate generation. In: Portuguese Conference on Artificial Intelligence, pp. 175–186. Springer (2013)

    Google Scholar 

  19. Cardoso, N., Abreu, R.: An efficient distributed algorithm for computing minimal hitting sets. In: Proceedings of the 25th International Workshop on Principles of Diagnosis, DX, vol. 14, p. 23 (2014)

    Google Scholar 

  20. Ceballos, R., Gasca, R.M., Borrego, D.: Constraint satisfaction techniques for diagnosing errors in design by contract software. ACM SIGSOFT Softw. Eng. Notes 31(2) (2006)

    Article  Google Scholar 

  21. Ceballos, R., Gasca, R.M., Valle, C.D., Borrego, D.: Diagnosing errors in dbc programs using constraint programming. In: Current Topics in Artificial Intelligence, 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, Santiago de Compostela, Spain, Revised Selected Papers, pp. 200–210 (2005)

    Chapter  Google Scholar 

  22. Ceballos, R., Gasca, R.M., Valle, C.D., Rosa, F.D.L.: A constraint programming approach for software diagnosis. In: Proceedings of the Fifth International Workshop on Automated Debugging, AADEBUG 2003, pp. 187–197 (2003)

    Google Scholar 

  23. Ceballos, R., Gasca, R.M., Valle, C.D., Toro, M.: Max-csp approach for software diagnosis. In: Advances in Artificial Intelligence - IBERAMIA 2002, 8th Ibero-American Conference on AI, pp. 172–181 (2002)

    Google Scholar 

  24. Czarnecki, K., Helsen, S., Eisenecker, U.: Formalizing cardinality-based feature models and their specialization. In: Software Process: Improvement and Practice, p. 2005 (2005)

    Article  Google Scholar 

  25. De Kleer, J.: Diagnosing multiple persistent and intermittent faults. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI’09, pp. 733–738 (2009)

    Google Scholar 

  26. Ernst, M.D., Perkins, J.H., Guo, P.J., McCamant, S., Pacheco, C., Tschantz, M.S., Xiao, C.: The daikon system for dynamic detection of likely invariants. Sci. Comput. Program. 69(1–3), 35–45 (2007)

    Article  MathSciNet  Google Scholar 

  27. Gouveia, C., Campos, J., Abreu, R.: Using HTML5 visualizations in software fault localization. In: 2013 First IEEE Working Conference on Software Visualization (VISSOFT), pp. 1–10. IEEE (2013)

    Google Scholar 

  28. Hangal, S., Lam, M.S.: Tracking down software bugs using automatic anomaly detection. In: Proceedings of the 24th International Conference on Software Engineering, ICSE 2002, pp. 291–301. IEEE (2002)

    Google Scholar 

  29. Hao, D., Zhang, L., Zhang, L., Sun, J., Mei, H.: Vida: Visual interactive debugging. In: IEEE 31st International Conference on Software Engineering, ICSE 2009, pp. 583–586. IEEE (2009)

    Google Scholar 

  30. Harrold, M.J., Rothermel, G., Sayre, K., Wu, R., Yi, L.: An empirical investigation of the relationship between fault-revealing test behavior and differences in program spectra. STVR J. Softw. Test., Verif., Reliab. 3, 171–194 (2000)

    Article  Google Scholar 

  31. Hofer, B., Riboira, A., Wotawa, F., Abreu, R., Getzner, E.: On the empirical evaluation of fault localization techniques for spreadsheets. In: ICSE ’13: Proceedings of the 2013 International Conference on Software Engineering, pp. 68–82. Springer (2013)

    Google Scholar 

  32. Jones, C.: The Year 2000 Software Problem: Quantifying the Costs and Assessing the Consequences. ACM Press/Addison-Wesley Publishing Co, Boston (1997)

    Google Scholar 

  33. Jones, J.A., Harrold, M.J., Stasko, J.: Visualization of test information to assist fault localization. In: ICSE ’02: Proceedings of the 24th International Conference on Software Engineering, pp. 467–477. IEEE (2002)

    Google Scholar 

  34. Kang, K.: Feature-oriented domain analysis (FODA): feasibility study. Technical Report CMU/SEI-90-TR-21 - ESD-90-TR-222, Carnegie Mellon University, Software Engineering Institute (1990)

    Google Scholar 

  35. de Kleer, J., Mackworth, A.K., Reiter, R.: Characterizing diagnoses and systems. Artif. Intell. 56(2–3), 197–222 (1992)

    Article  MathSciNet  Google Scholar 

  36. Kremenek, T., Ashcraft, K., Yang, J., Engler, D.: Correlation exploitation in error ranking. In: ACM SIGSOFT Software Engineering Notes, vol. 29, pp. 83–93. ACM (2004)

    Google Scholar 

  37. Lieberman, H.: The debugging scandal and what to do about it. Commun. ACM 40(4), 26–30 (1997)

    Article  Google Scholar 

  38. Liu, C., Yan, X., Fei, L., Han, J., Midkiff, S.P.: Sober: Statistical model-based bug localization. In: Proceeding of the ESEC/FSE-13. ACM, Lisbon, Portugal (2005)

    Google Scholar 

  39. Mayer, W., Stumptner, M.: Models and tradeoffs in model-based debugging. In: 18th International Workshop on Principles of Diagnosis. Nashville, TN, USA (2007)

    Google Scholar 

  40. Mayer, W., Stumptner, M.: Evaluating models for model-based debugging. In: Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering, pp. 128–137. IEEE Computer Society (2008)

    Google Scholar 

  41. Meyer, B.: Applying design by contract. IEEE Comput. 25(10), 40–51 (1992)

    Article  Google Scholar 

  42. Pearson, S., Campos, J., Just, R., Fraser, G., Abreu, R., Ernst, M.D., Pang, D., Keller, B.: Evaluating and improving fault localization. In: ICSE ’17: Proceedings of the 39th International Conference on Software Engineering, pp. 609–620. IEEE Press (2017)

    Google Scholar 

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

    Google Scholar 

  44. Perez, A., Abreu, R., van Deursen, A.: A test-suite diagnosability metric for spectrum-based fault localization approaches. In: Proceedings of the 39th International Conference on Software Engineering, pp. 654–664. IEEE Press (2017)

    Google Scholar 

  45. Perez, A., Abreu, R., HASLab, I.T.: Leveraging qualitative reasoning to improve SFL. In: IJCAI, pp. 1935–1941 (2018)

    Google Scholar 

  46. Renieres, M., Reiss, S.P.: Fault localization with nearest neighbor queries. In: Proceedings of the 18th IEEE International Conference on Automated Software Engineering, 2003, pp. 30–39. IEEE (2003)

    Google Scholar 

  47. Shepard, T., Lamb, M., Kelly, D.: More testing should be taught. Commun. ACM 44(6), 103–108 (2001)

    Article  Google Scholar 

  48. Steimann, F., Frenkel, M., Abreu, R.: Threats to the validity and value of empirical assessments of the accuracy of coverage-based fault locators. In: Proceedings of the 2013 International Symposium on Software Testing and Analysis, pp. 314–324. ACM (2013)

    Google Scholar 

  49. Tip, F.: A Survey of Program Slicing Techniques. Centrum voor Wiskunde en Informatica, Amsterdam (1994)

    MATH  Google Scholar 

  50. Traon, Y.L., Ouabdesselam, F., Robach, C., Baudry, B.: From diagnosis to diagnosability: axiomatization, measurement and application. J. Syst. Softw. 65(1), 31–50 (2003)

    Article  Google Scholar 

  51. Varela-Vaca, Á.J., Gasca, R.M.: Towards the automatic and optimal selection of risk treatments for business processes using a constraint programming approach. Inf. Softw. Technol. 55(11), 1948–1973 (2013)

    Article  Google Scholar 

  52. Wong, W.E., Gao, R., Li, Y., Abreu, R., Wotawa, F.: A survey on software fault localization. IEEE Trans. Softw. Eng. 42(8), 707–740 (2016)

    Article  Google Scholar 

  53. Wotawa, F., Stumptner, M., Mayer, W.: Model-based debugging or how to diagnose programs automatically. In: Hendtlass, T., Ali, M. (eds.) Developments in Applied Artificial Intelligence, pp. 746–757. Springer, Berlin (2002)

    Chapter  Google Scholar 

  54. Wotawa, F., Weber, J., Nica, M., Ceballos, R.: On the complexity of program debugging using constraints for modeling the program’s syntax and semantics. In: Current Topics in Artificial Intelligence, 13th Conference of the Spanish Association for Artificial Intelligence, CAEPIA. Selected Papers, pp. 22–31 (2009)

    Chapter  Google Scholar 

  55. Yang, Q., Li, J.J., Weiss, D.: A survey of coverage based testing tools. In: Proceedings of the 2006 International Workshop on Automation of software test, AST ’06, pp. 99–103. ACM, New York, NY, USA (2006)

    Google Scholar 

  56. Yilmaz, C., Williams, C.: An automated model-based debugging approach. In: ASE ’07: Proceedings of the Twenty-Second IEEE/ACM International Conference on Automated Software Engineering, pp. 174–183. ACM (2007)

    Google Scholar 

  57. Zoeteweij, P., Abreu, R., Golsteijn, R., Van Gemund, A.J.: Diagnosis of embedded software using program spectra. In: 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS’07), pp. 213–220. IEEE (2007)

    Google Scholar 

Download references

Acknowledgements

This work has been partially funded by the Ministry of Science and Technology of Spain (TIN2015-63502-C3-2-R) and the European Regional Development Fund (ERDF/ FEDER). This material is based upon work supported by the ERDF’s COMPETE 2020 Programme under project No. POCI-01-0145-FEDER-006961 and FCT under project No. UID/EEA/50014/ 2013.

Author information

Authors and Affiliations

  1. University of Seville, Seville, Spain

    Rafael Ceballos, Ángel Jesús Varela-Vaca & Rafael M. Gasca

  2. IST, University of Lisbon and INESC-ID, Lisbon, Portugal

    Rui Abreu

Authors
  1. Rafael Ceballos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Abreu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ángel Jesús Varela-Vaca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rafael M. Gasca
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Ceballos .

Editor information

Editors and Affiliations

  1. Research Center for Supervision, Safety and Automatic Control (CS2AC), Universitat Politècnica de Catalunya (UPC), Terrassa, Spain

    Teresa Escobet

  2. Department of Computer Science, University of Valladolid, Segovia, Spain

    Anibal Bregon

  3. Department of Computer Science, University of Valladolid, Valladolid, Spain

    Belarmino Pulido

  4. Research Center for Supervision, Safety and Automatic Control (CS2AC), Universitat Politècnica de Catalunya (UPC), Terrassa, Spain

    Vicenç Puig

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ceballos, R., Abreu, R., Varela-Vaca, Á.J., Gasca, R.M. (2019). Model-Based Software Debugging. In: Escobet, T., Bregon, A., Pulido, B., Puig, V. (eds) Fault Diagnosis of Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-17728-7_15

Download citation

Publish with us

Policies and ethics

Search

Navigation