Skip to main content
SpringerLink
Log in
Book cover

European Conference on the Applications of Evolutionary Computation

EvoApplications 2017: Applications of Evolutionary Computation pp 20–34Cite as

On the Use of Smelly Examples to Detect Code Smells in JavaScript

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10200))

Abstract

JavaScript has become one of the widely-used languages. However, as the size of JavaScript-based applications grows, the number of defects grows as well. Recent studies have produced a set of manually defined rules to identify these defects. We propose, in this work, the automation of deriving these rules to ensure scalability and potentially the detection of a wider set of defects without requiring any extensive knowledge on rules tuning. To this end, we rely on a base of existing code smells that is used to train the detection rules using Genetic Programming and find the best threshold of metrics composing the rules. The evaluation of our work on 9 JavaScript web projects has shown promising results in terms of detection precision of 92% and recall of 85%, with no threshold tuning required.

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

Notes

  1. 1.

    For replication purposes, the dataset and tools used are located in: https://github.com/mkaouer/Code-Smells-Detection-in-JavaScript.

References

  1. Richards, G., Lebresne, S., Burg, B., Vitek, J.: An analysis of the dynamic behavior of JavaScript programs. In: Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2010, Toronto, Ontario, Canada, pp. 1–12 (2010)

    Google Scholar 

  2. Richards, G., Hammer, C., Burg, B., Vitek, J.: The eval that men do. In: Mezini, M. (ed.) ECOOP 2011. LNCS, vol. 6813, pp. 52–78. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22655-7_4

    Chapter  Google Scholar 

  3. Ratanaworabhan, P., Livshits, B., Zorn, B.G.: JSMeter: comparing the behavior of JavaScript benchmarks with real web applications. In: WebApps 2010, vol. 10, p. 3 (2010)

    Google Scholar 

  4. Nikiforakis, N., Invernizzi, L., Kapravelos, A., Van Acker, S., Joosen, W., Kruegel, C., Piessens, F., Vigna, G.: You are what you include: large-scale evaluation of remote JavaScript inclusions. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, CCS 2012, Raleigh, North Carolina, USA, pp. 736–747 (2012)

    Google Scholar 

  5. Guarnieri, S., Pistoia, M., Tripp, O., Dolby, J., Teilhet, S., Berg, R.: Saving the world wide web from vulnerable JavaScript. In: Proceedings of the 2011 International Symposium on Software Testing and Analysis, ISSTA 2011, Toronto, Ontario, Canada, pp. 177–187 (2011)

    Google Scholar 

  6. Fowler, M., Beck, K., Brant, J., Opdyke, W., Roberts, D.: Refactoring: improving the design of existing programs. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)

    Google Scholar 

  7. Madsen, M., Tip, F., Andreasen, E., Sen, K., Møller, A.: Feedback-directed instrumentation for deployed JavaScript applications. In: Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, Austin, Texas, pp. 899–910 (2016)

    Google Scholar 

  8. Crockford, D.: JSLint: The javascript code quality tool, 95 (2011). http://www.jslint.com

  9. Kovalyov, A., Kluge, W., Perez, J.: JSHint, a JavaScript code quality tool (2010)

    Google Scholar 

  10. Pradel, M., Schuh, P., Sen, K.: TypeDevil: Dynamic type inconsistency analysis for JavaScript. In: Proceedings of the 37th International Conference on Software Engineering, ICSE 2015, Florence, Italy, pp. 314–324 (2015)

    Google Scholar 

  11. Raychev, V., Vechev, M., Sridharan, M.: Effective race detection for event-driven programs. In: Proceedings of the 2013 ACM SIGPLAN International Conference, OOPSLA 2013, Indianapolis, Indiana, USA, pp. 151–166 (2013)

    Google Scholar 

  12. Mesbah, A., Prasad, M.R.: Automated cross-browser compatibility testing. In: Proceedings of the 33rd International Conference on Software Engineering, ICSE 2011, Waikiki, Honolulu, HI, USA, pp. 561–570 (2011)

    Google Scholar 

  13. Fard, A.M., Mesbah, A.: JSNOSE: detecting JavaScript code smells. In: Proceedings of the 13th IEEE International Conference on Source Code Analysis and Manipulation, SCAM 2013, pp. 116–125 (2013)

    Google Scholar 

  14. Marinescu, R., Ganea, G., Verebi, I.: inCode: continuous quality assessment and improvement. In: Proceedings of the 14th European Conference on Software Maintenance and Reengineering, pp. 274–275 (2010)

    Google Scholar 

  15. Palomba, F., Bavota, G., Di Penta, M., Oliveto, R., De Lucia, A., Poshyvanyk, D.: Detecting bad smells in source code using change history information. In: 2013 IEEE/ACM 28th International Conference on Automated Software Engineering (2013)

    Google Scholar 

  16. Palomba, F.: Textual analysis for code smell detection. In: Proceedings of the 37th International Conference on Software Engineering, ICSE 2015, Florence, Italy, pp. 769–771 (2015)

    Google Scholar 

  17. Garrett, J.J.: Ajax: a new approach to web applications (2005)

    Google Scholar 

  18. Cornelissen, B., Zaidman, A., Van Deursen, A., Moonen, L., Koschke, R.: A systematic survey of program comprehension through dynamic analysis. IEEE Transactions on Software Engineering 35(5), 684–702

    Google Scholar 

  19. Silva, L.H., Ramos, M., Valente, M.T., Bergel, A., Anquetil, N.: Does JavaScript software embrace classes? In: Proceedings of the 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 73–82 (2015)

    Google Scholar 

  20. Mendes, T., Valente, M.T., Hora, A.: Identifying utility functions in Java and JavaScript. In: 2016 X Brazilian Symposium on Software Components, Architectures and Reuse (SBCARS), 19–20 September 2016, pp. 121–130 (2016)

    Google Scholar 

  21. Humberto Silva, L., Hovadick, D., Tulio Valente, M., Bergel, A., Anquetil, N., Etien, A.: JSClassFinder: a tool to detect class-like structures in JavaScript. arXiv preprint arXiv:1602.05891 (2016)

  22. Merlo, E., Antoniol, G., Di Penta, M., Rollo, V.F.: Linear complexity object-oriented similarity for clone detection and software evolution analyses. In: Proceedings of IEEE 20th International Conference on Software Maintenance, ICSM 2004, pp. 412–416 (2004)

    Google Scholar 

  23. Van Belle, T., Ackley, D.H.: Uniform subtree mutation, pp. 152–161 (2002)

    Google Scholar 

  24. http://pmd.sourceforge.net/snapshot/. Accessed 17 July 2016

  25. Van Emden, E., Moonen, L.: Java quality assurance by detecting code smells. In: Proceedings of the 9th Working Conference on Reverse Engineering (WCRE 2002), Washington, DC, USA, pp. 97–106 (2002)

    Google Scholar 

  26. Mäntylä, M., Vanhanen, J., Lassenius, C.: A taxonomy and an initial empirical study of bad smells in code. Proceedings of the International Conference on Software Maintenance, ICSM 2003, 381–384 (2003)

    Google Scholar 

  27. Marinescu, R.: Detection strategies: metrics-based rules for detecting design flaws, pp. 350–359 (2004)

    Google Scholar 

  28. Moha, N., Gueheneuc, Y.-G., Duchien, L., Le Meur, A.-F.: DECOR: a method for the specification and detection of code and design smells. IEEE Trans. Software Eng. 36(1), 20–36 (2010)

    Article  MATH  Google Scholar 

  29. Kessentini, M., Kessentini, W., Sahraoui, H., Boukadoum, M., Ouni, A.: Design defects detection and correction by example. In: Proceedings of the 19th IEEE International Conference on Program Comprehension (ICPC 2011), 22–24 June 2011, pp. 81–90 (2011)

    Google Scholar 

  30. Ouni, A., Kessentini, M., Sahraoui, H., Boukadoum, M.: Maintainability defects detection and correction: a multi-objective approach. Automated Software Engineering 20(1), 47–79 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, Rochester Institute of Technology, Rochester, USA

    Ian Shoenberger & Mohamed Wiem Mkaouer

  2. Department of Computer and Information Science, University of Michigan, Ann Arbor, USA

    Marouane Kessentini

Authors
  1. Ian Shoenberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Wiem Mkaouer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marouane Kessentini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Wiem Mkaouer .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Shoenberger, I., Mkaouer, M.W., Kessentini, M. (2017). On the Use of Smelly Examples to Detect Code Smells in JavaScript. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10200. Springer, Cham. https://doi.org/10.1007/978-3-319-55792-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55792-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55791-5

  • Online ISBN: 978-3-319-55792-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation