Using Daikon to Prioritize and Group Unit Bugs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8348)


Unit testing and verification constitute an important step in the validation life cycle of large and complex multi-component software code bases. Many unit validation methods often suffer from the problem of false failure alarms, when they analyse a component in isolation and look for errors. It often turns out that some of the reported unit failures are infeasible, i.e. the valuations of the component input parameters that trigger the failure, though feasible on the unit module in isolation, cannot occur in practice considering the integrated code, in which the unit-under-test is instantiated. In this paper, we consider this problem in the context of a multi-function software code base, with a set of unit level failures reported on a specific function. We present here an automated two-stage failure classification and prioritization strategy that can filter out false alarms and classify them accordingly. Early experiments show interesting results.


False Alarm Confidence Measure Execution Trace Symbolic Execution Program Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by The Open Project of Shanghai Key Laboratory of Trustworthy Computing (No. 07dz22304201201).


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    The yices smt solver.
  5. 5.
    Burnim, J., Sen, K.: Heuristics for scalable dynamic test generation. In: ASE, pp. 443–446 (2008)Google Scholar
  6. 6.
    Chaki, S., Clarke, E., Giannakopoulou, D., Psreanu, C.S.: Abstraction and assume-guarantee reasoning for automated software verification. Technical report (2004)Google Scholar
  7. 7.
    Dang, Y., Wu, R., Zhang, H., Zhang, D., Nobel, P.: Rebucket: a method for clustering duplicate crash reports based on call stack similarity. In: ICSE, pp. 1084–1093 (2012)Google Scholar
  8. 8.
    Elbaum, S.G., Malishevsky, A.G., Rothermel, G.: Test case prioritization: a family of empirical studies. IEEE Trans. Softw. Eng. 28(2), 159–182 (2002)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Ghosh, A.K., Chaudhuri, P., Murthy, C.A.: On visualization and aggregation of nearest neighbor classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1592–1602 (2005)CrossRefGoogle Scholar
  11. 11.
    Henzinger, T.A., Jhala, R., MAjumdar, R., Sutre, G.: Lazy abstraction. In: POPL, pp. 58–70 (2002)Google Scholar
  12. 12.
    Kim, D., Wang, X., Kim, S., Zeller, A., Cheung, S.C., Park, S.: Which crashes should I fix first?: predicting top crashes at an early stage to prioritize debugging efforts. IEEE Trans. Softw. Eng. 37(3), 430–447 (2011)CrossRefGoogle Scholar
  13. 13.
    Kremenek, T., Ashcraft, K., Yang, J., Engler, D.R.: Correlation exploitation in error ranking. In: SIGSOFT FSE, pp. 83–93 (2004)Google Scholar
  14. 14.
    Mitra, S., Banerjee, A., Dasgupta, P.: Formal methods for ranking counterexamples through assumption mining. In: DATE, pp. 911–916 (2012)Google Scholar
  15. 15.
    Rothermel, G., Untch, R.H., Chu, C., Harrold, M.J.: Test case prioritization: an empirical study. In: ICSM, pp. 179–188 (1999)Google Scholar
  16. 16.
    Sen, K., Agha, G.: CUTE and jCUTE: concolic unit testing and explicit path model-checking tools. In: Ball, T., Jones, R.B. (eds.) CAV 2006. LNCS, vol. 4144, pp. 419–423. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  17. 17.
    Sen, K., Marinov, D., Agha, G.: Cute: a concolic unit testing engine for c. In: ESEC/SIGSOFT FSE, pp. 263–272 (2005)Google Scholar
  18. 18.
    Seo, H., Kim, S.: Predicting recurring crash stacks. In: ASE, pp. 180–189 (2012)Google Scholar
  19. 19.
    Shen, H., Fang, J., Zhao, J.: Efindbugs: effective error ranking for findbugs. In: ICST, pp. 299–308 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Indian Statistical InstituteKolkataIndia
  2. 2.Jadavpur UniversityKolkataIndia
  3. 3.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  4. 4.Software Engineering InstituteEast China Normal UniversityShanghaiChina

Personalised recommendations