Skip to main content
Springer Nature Link
Log in

Reducing Static Analysis Alarms Based on Non-impacting Control Dependencies

  • Conference paper
  • First Online:
Programming Languages and Systems (APLAS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11893))

Included in the following conference series:

Abstract

Static analysis tools help to detect programming errors but generate a large number of alarms. Repositioning of alarms is recently proposed technique to reduce the number of alarms by replacing a group of similar alarms with a small number of newly created representative alarms. However, the technique fails to replace a group of similar alarms with a fewer representative alarms mainly when the immediately enclosing conditional statements of the alarms are different and not nested. This limitation is due to conservative assumption that a conditional statement of an alarm may prevent the alarm from being an error.

To address the limitation above, we introduce the notion of non-impacting control dependencies (NCDs). An NCD of an alarm is a transitive control dependency of the alarm’s program point, that does not affect whether the alarm is an error. We approximate the computation of NCDs based on the alarms that are similar, and then reposition the similar alarms by considering the effect of their NCDs. The NCD-based repositioning allows to merge more similar alarms together and represent them by a small number of representative alarms than the state-of-the-art repositioning technique. Thus, it can be expected to further reduce the number of alarms.

To measure the reduction obtained, we evaluate the NCD-based repositioning using total 105,546 alarms generated on 16 open source C applications, 11 industry C applications, and 5 industry COBOL applications. The evaluation results indicate that, compared to the state-of-the-art repositioning technique, the NCD-based repositioning reduces the number of alarms respectively by up to 23.57%, 29.77%, and 36.09%. The median reductions are 9.02%, 17.18%, and 28.61%, respectively.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Broadly, two alarms are said to be similar if the property/condition checked in one alarm implies the property/condition checked in the other alarm (Sect. 2).

  2. 2.

    A control dependency of a program point p is a conditional edge in the control flow graph [1], that decides whether p is to be reached or not (see Sect. 2).

  3. 3.

    Note that the related original alarms (relOrigAlarms) of a liveCond \(\ell \) are computed corresponding to its reposLocations (reposLocation-wise).

References

  1. Allen, F.E.: Control flow analysis. In: Symposium on Compiler Optimization, pp. 1–19. ACM, New York (1970)

    Google Scholar 

  2. Ayewah, N., Pugh, W.: The Google FindBugs fixit. In: International Symposium on Software Testing and Analysis, pp. 241–252. ACM, New York (2010)

    Google Scholar 

  3. Ayewah, N., Pugh, W., Morgenthaler, J.D., Penix, J., Zhou, Y.: Evaluating static analysis defect warnings on production software. In: Workshop on Program Analysis for Software Tools and Engineering, pp. 1–8. ACM, New York (2007)

    Google Scholar 

  4. Beller, M., Bholanath, R., McIntosh, S., Zaidman, A.: Analyzing the state of static analysis: a large-scale evaluation in open source software. In: International Conference on Software Analysis, Evolution, and Reengineering, vol. 1, pp. 470–481 (2016)

    Google Scholar 

  5. Bessey, A., et al.: A few billion lines of code later: using static analysis to find bugs in the real world. Commun. ACM 53(2), 66–75 (2010)

    Article  Google Scholar 

  6. Brat, G., Venet, A.: Precise and scalable static program analysis of NASA flight software. In: 2005 IEEE Aerospace Conference, pp. 1–10, March 2005

    Google Scholar 

  7. Christakis, M., Bird, C.: What developers want and need from program analysis: an empirical study. In: International Conference on Automated Software Engineering, pp. 332–343. ACM, New York (2016)

    Google Scholar 

  8. Cousot, P., Cousot, R., Fähndrich, M., Logozzo, F.: Automatic inference of necessary preconditions. In: Giacobazzi, R., Berdine, J., Mastroeni, I. (eds.) VMCAI 2013. LNCS, vol. 7737, pp. 128–148. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35873-9_10

    Chapter  Google Scholar 

  9. Cytron, R., Ferrante, J., Rosen, B.K., Wegman, M.N., Zadeck, F.K.: Efficiently computing static single assignment form and the control dependence graph. ACM Trans. Program. Lang. Syst. 13(4), 451–490 (1991)

    Article  Google Scholar 

  10. Denney, E., Trac, S.: A software safety certification tool for automatically generated guidance, navigation and control code. In: 2008 IEEE Aerospace Conference, pp. 1–11, March 2008

    Google Scholar 

  11. Dillig, I., Dillig, T., Aiken, A.: Automated error diagnosis using abductive inference. In: Conference on Programming Language Design and Implementation, pp. 181–192. ACM, New York (2012)

    Article  Google Scholar 

  12. Ferrante, J., Ottenstein, K.J., Warren, J.D.: The program dependence graph and its use in optimization. ACM Trans. Program. Lang. Syst. 9(3), 319–349 (1987)

    Article  Google Scholar 

  13. Gehrke, M.: Bidirectional Predicate Propagation in Frama-C and its Application to Warning Removal. Master’s thesis, Hamburg University of Technology (2014)

    Google Scholar 

  14. Heckman, S., Williams, L.: A systematic literature review of actionable alert identification techniques for automated static code analysis. Inf. Softw. Technol. 53(4), 363–387 (2011)

    Article  Google Scholar 

  15. Johnson, B., Song, Y., Murphy-Hill, E., Bowdidge, R.: Why don’t software developers use static analysis tools to find bugs? In: International Conference on Software Engineering, pp. 672–681. IEEE Press, Piscataway (2013)

    Google Scholar 

  16. Khedker, U., Sanyal, A., Sathe, B.: Data Flow Analysis: Theory and Practice. CRC Press, Boca Raton (2009)

    Book  Google Scholar 

  17. Kornecki, A., Zalewski, J.: Certification of software for real-time safety-critical systems: state of the art. Innov. Syst. Softw. Eng. 5(2), 149–161 (2009)

    Article  Google Scholar 

  18. Kumar, S., Sanyal, A., Khedker, U.P.: Value slice: a new slicing concept for scalable property checking. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 101–115. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46681-0_7

    Chapter  MATH  Google Scholar 

  19. Layman, L., Williams, L., St. Amant, R.: Toward reducing fault fix time: understanding developer behavior for the design of automated fault detection tools. In: International Symposium on Empirical Software Engineering and Measurement, pp. 176–185 (2007)

    Google Scholar 

  20. Lee, W., Lee, W., Kang, D., Heo, K., Oh, H., Yi, K.: Sound non-statistical clustering of static analysis alarms. ACM Trans. Program. Lang. Syst. 39(4), 16:1–16:35 (2017)

    Article  Google Scholar 

  21. Lee, W., Lee, W., Yi, K.: Sound non-statistical clustering of static analysis alarms. In: Kuncak, V., Rybalchenko, A. (eds.) VMCAI 2012. LNCS, vol. 7148, pp. 299–314. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27940-9_20

    Chapter  Google Scholar 

  22. Mangal, R., Zhang, X., Nori, A.V., Naik, M.: A user-guided approach to program analysis. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, pp. 462–473. ACM, New York (2015)

    Google Scholar 

  23. Meyer, B.: Design by Contract. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  24. Muske, T., Baid, A., Sanas, T.: Review efforts reduction by partitioning of static analysis warnings. In: International Working Conference on Source Code Analysis and Manipulation, pp. 106–115 (2013)

    Google Scholar 

  25. Muske, T., Khedker, U.P.: Cause points analysis for effective handling of alarms. In: International Symposium on Software Reliability Engineering, pp. 173–184, October 2016

    Google Scholar 

  26. Muske, T., Serebrenik, A.: Survey of approaches for handling static analysis alarms. In: International Working Conference on Source Code Analysis and Manipulation, pp. 157–166 (2016)

    Google Scholar 

  27. Muske, T., Talluri, R., Serebrenik, A.: Repositioning of static analysis alarms. In: Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2018, pp. 187–197. ACM, New York (2018)

    Google Scholar 

  28. Nielson, F., Nielson, H.R., Hankin, C.: Principles of Program Analysis. Springer, New York (1999). https://doi.org/10.1007/978-3-662-03811-6

    Book  MATH  Google Scholar 

  29. Rival, X.: Abstract dependences for alarm diagnosis. In: Yi, K. (ed.) APLAS 2005. LNCS, vol. 3780, pp. 347–363. Springer, Heidelberg (2005). https://doi.org/10.1007/11575467_23

    Chapter  Google Scholar 

  30. Rival, X.: Understanding the origin of alarms in Astrée. In: Hankin, C., Siveroni, I. (eds.) SAS 2005. LNCS, vol. 3672, pp. 303–319. Springer, Heidelberg (2005). https://doi.org/10.1007/11547662_21

    Chapter  Google Scholar 

  31. Sadowski, C., van Gogh, J., Jaspan, C., Söderberg, E., Winter, C.: Tricorder: building a program analysis ecosystem. In: International Conference on Software Engineering, pp. 598–608. IEEE Press, Piscataway (2015)

    Google Scholar 

  32. TCS Embedded Code Analyzer (TCS ECA). https://www.tcs.com/tcs-embedded-code-analyzer. Accessed 30 Aug 2019

  33. Venet, A.: A practical approach to formal software verification by static analysis. Ada Lett. XXVII I(1), 92–95 (2008)

    Article  Google Scholar 

  34. Zhang, D., Jin, D., Gong, Y., Zhang, H.: Diagnosis-oriented alarm correlations. In: Asia-Pacific Software Engineering Conference, vol. 1, pp. 172–179 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TRDDC, TCS Research, Pune, India

    Tukaram Muske & Rohith Talluri

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Alexander Serebrenik

Authors
  1. Tukaram Muske
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rohith Talluri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Serebrenik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tukaram Muske .

Editor information

Editors and Affiliations

  1. University of Kaiserslautern, Kaiserslautern, Germany

    Anthony Widjaja Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Muske, T., Talluri, R., Serebrenik, A. (2019). Reducing Static Analysis Alarms Based on Non-impacting Control Dependencies. In: Lin, A. (eds) Programming Languages and Systems. APLAS 2019. Lecture Notes in Computer Science(), vol 11893. Springer, Cham. https://doi.org/10.1007/978-3-030-34175-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34175-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34174-9

  • Online ISBN: 978-3-030-34175-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics