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

  • Ian Shoenberger
  • Mohamed Wiem Mkaouer
  • Marouane Kessentini
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

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References

  1. 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. 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_4CrossRefGoogle Scholar
  3. 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. 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. 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. 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. 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. 8.
    Crockford, D.: JSLint: The javascript code quality tool, 95 (2011). http://www.jslint.com
  9. 9.
    Kovalyov, A., Kluge, W., Perez, J.: JSHint, a JavaScript code quality tool (2010)Google Scholar
  10. 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. 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. 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. 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. 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. 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. 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. 17.
    Garrett, J.J.: Ajax: a new approach to web applications (2005)Google Scholar
  18. 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), 684–702Google Scholar
  19. 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. 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. 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. 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. 23.
    Van Belle, T., Ackley, D.H.: Uniform subtree mutation, pp. 152–161 (2002)Google Scholar
  24. 24.
  25. 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. 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. 27.
    Marinescu, R.: Detection strategies: metrics-based rules for detecting design flaws, pp. 350–359 (2004)Google Scholar
  28. 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)CrossRefMATHGoogle Scholar
  29. 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. 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ian Shoenberger
    • 1
  • Mohamed Wiem Mkaouer
    • 1
  • Marouane Kessentini
    • 2
  1. 1.Department of Software EngineeringRochester Institute of TechnologyRochesterUSA
  2. 2.Department of Computer and Information ScienceUniversity of MichiganAnn ArborUSA

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