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Multi-objective code-smells detection using good and bad design examples

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Abstract

Code-smells are identified, in general, by using a set of detection rules. These rules are manually defined to identify the key symptoms that characterize a code-smell using combinations of mainly quantitative (metrics), structural, and/or lexical information. We propose in this work to consider the problem of code-smell detection as a multi-objective problem where examples of code-smells and well-designed code are used to generate detection rules. To this end, we use multi-objective genetic programming (MOGP) to find the best combination of metrics that maximizes the detection of code-smell examples and minimizes the detection of well-designed code examples. We evaluated our proposal on seven large open-source systems and found that, on average, most of the different five code-smell types were detected with an average of 87 % of precision and 92 % of recall. Statistical analysis of our experiments over 51 runs shows that MOGP performed significantly better than state-of-the-art code-smell detectors.

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Notes

  1. http://ant.apache.org/.

  2. http://argouml.tigris.org/.

  3. www.ganttproject.biz.

  4. http://vuze.com.

  5. http://xerces.apache.org/xerces-j/.

  6. http://www.jhotdraw.org/.

  7. http://staff.unak.is/andy/StaticAnalysis0809/metrics/overview.html.

References

  • Abbes, M., Khomh, F., Gueheneuc, Y.-G., & Antoniol, G. (2011). An empirical study of the impact of two antipatterns, blob and spaghetti code, on program comprehension. In Software maintenance and reengineering (CSMR), 2011 15th European conference on (pp. 181–190). IEEE.

  • Abreu, F., Goulão, M., & Esteves, R. (1995). Toward the design quality evaluation of object-oriented software systems. In Proceedings of 5th ICSQ.

  • Aghezzaf, B., & Hachimi, M. (2000). Generalized invexity and duality in multiobjective programming problems. Journal of Global Optimization, 18(1), 91–101.

    Article  MathSciNet  MATH  Google Scholar 

  • Al Dallal, J. (2014). Identifying refactoring opportunities in object-oriented code: A systematic literature review. Information and Software Technology, 58, 231–249.

    Article  Google Scholar 

  • Al Dallal, J. (2015). Identifying refactoring opportunities in object-oriented code: A systematic literature review. Information and Software Technology, 58, 231–249.

    Article  Google Scholar 

  • Arcuri, A., & Briand, L. C. (2011). A practical guide for using statistical tests to assess randomized algorithms in software engineering. In Proceedings of the 33rd international conference on software engineering (ICSE) (pp. 1–10).

  • Bavota, G., De Lucia, A., Di Penta, M., Oliveto, R., & Palomba, F. (2015). An experimental investigation on the innate relationship between quality and refactoring. Journal of Systems and Software, 107, 1–14.

    Article  Google Scholar 

  • Brown, W. J., Malveau, R. C., Brown, W. H., & Mowbray, T. J. (1998). Anti-patterns: Refactoring software, architectures, and projects in crisis. Hoboken: Wiley.

    Google Scholar 

  • Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 20(6), 293–318.

    Article  Google Scholar 

  • Concas, G., Destefanis, G., Marchesi, M., Ortu, M., & Tonelli, R. (2013). Micro patterns in agile software. Berlin: Springer.

    Book  Google Scholar 

  • Deb, K. (2001). Multiobjective optimization using evolutionary algorithms. New York: Wiley.

    MATH  Google Scholar 

  • Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

  • Destefanis, G., Tonelli, R., Tempero, E., Concas, G., & Marchesi, M. (2012, September). Micro pattern fault-proneness. In Software engineering and advanced applications (SEAA), 2012 38th EUROMICRO conference on (pp. 302–306). IEEE.

  • Dhambri, K., Sahraoui, H. A., & Poulin, P. (2008). Visual detection of design anomalies. In CSMR. IEEE (pp. 279–283).

  • Fenton, N., & Pfleeger, S. L. (1998). Software metrics: A rigorous and practical approach (2nd ed.). London: International Thomson Computer Press.

    Google Scholar 

  • Fontana, F. A., Mäntylä, M. V., Zanoni, M., & Marino, A. (2015). Comparing and experimenting machine learning techniques for code smell detection. In Empirical Software Engineering (pp. 1–49).

  • Fowler, M., Beck, K., Brant, J., Opdyke, W., & Roberts, D. (1999). Refactoring—Improving the design of existing code. Boston: Addison-Wesley Professional.

    Google Scholar 

  • Gil, J. Y., & Maman, I. (2005). Micro patterns in Java code. In ACM SIGPLAN Notices (Vol. 40, no. 10). ACM.

  • Gong, M., Jiao, L., Du, H., & Bo, L. (2008). Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, 6(2), 225–255.

    Article  Google Scholar 

  • Hall, T., Zhang, M., Bowes, D., & Sun, Y. (2014). Some code smells have a significant but small effect on faults. ACM Transactions on Software Engineering and Methodology (TOSEM), 23(4), 33.

    Article  Google Scholar 

  • Harman, M., Mansouri, S. A., & Zhang, Y. (2012). Search-based software engineering: Trends, techniques and applications. ACM Computing Surveys, 45, 11.

    Article  Google Scholar 

  • Kessentini, M., Kessentini, W., Sahraoui, H., Boukadoum, M., & Ouni, A. (2011a). Design defects detection and correction by example. In Proceedings of the 19th IEEE international conference on program comprehension (ICPC’11) (pp. 81–90).

  • Kessentini, M., Kessentini, W., Sahraoui, H., Boukadoum, M., & Ouni, A. (2011b). Design defects detection and correction by example. In 19th IEEE international conference on program comprehension (ICPC), (22–24 June 2011), Kingston, Canada (pp. 81–90).

  • Kessentini, M., Vaucher, S., & Sahraoui, H. (2010). Deviance from perfection is a better criterion than closeness to evil when identifying risky code. In Proceedings of the 25th IEEE/ACM international conference on automated software engineering (ASE) (pp. 141–151).

  • Khomh, F., Vaucher, S., Guéhéneuc, Y.-G., & Sahraoui, H. (2009). A Bayesian approach for the detection of code and design smells. In Proceedings of the ICQS’09.

  • Kothari, S. C., Bishop, L., Sauceda, J., & Daugherty, G. (2004). A pattern-based framework for software anomaly detection. Software Quality Journal, 12(2), 99–120.

    Article  Google Scholar 

  • Kreimer, J. (2005). Adaptive detection of design flaws. Electronic Notes in Theoretical Computer Science, 141(4), 117–136.

    Article  Google Scholar 

  • Langdon, W. B., Poli, R., McPhee, N. F., & Koza, J. R. (2008). Genetic programming: An introduction and tutorial, with a survey of techniques and applications. In J. Fulcher & L. C. Jain (Eds.), Computational intelligence: A compendium (pp. 927–1028). Berlin: Springer.

    Chapter  Google Scholar 

  • Langelier, G., Sahraoui, H. A., & Poulin, P. (2005). Visualization-based analysis of quality for large-scale software systems. In T. Ellman & A. Zisma (Eds.), Proceedings of the 20th international conference on automated software engineering. New York: ACM Press.

  • Maggioni, S., & Arcelli, F. (2010). Metrics-based detection of micro patterns. In Proceedings of the 2010 ICSE workshop on emerging trends in software metrics. ACM.

  • Maiga, A., Ali, N., Bhattacharya, N., Sabane, A., Guéhéneuc, Y. G., & Aimeur, E. (2012, October). Smurf: A svm-based incremental anti-pattern detection approach. In Reverse engineering (WCRE), 2012 19th working conference on (pp. 466–475). IEEE.

  • Mäntylä, M. V. (2010). Empirical software evolvability—code smells and human evaluations. In ICSM (pp. 1–6).

  • Mäntylä, M., & Lassenius, C. (2006). Subjective evaluation of software evolvability using code smells: An empirical study. Empirical Software Engineering, 11(3), 395–431.

    Article  Google Scholar 

  • Marinescu, R. (2004). Detection strategies: Metrics-based rules for detecting design flaws. In Proceedings of ICM’04 (pp. 350–359).

  • Mkaouer, M. W., Kessentini, M., Bechikh, S., Cinnéide, M. Ó., & Deb, K. (2015). On the use of many quality attributes for software refactoring: A many-objective search-based software engineering approach. In Empirical Software Engineering (pp. 1–43).

  • Moha, N., Guéhéneuc, Y. G., Duchien, L., & Le Meur, A. F. (2010). DECOR: A method for the specification and detection of code and design smells. IEEE Transactions on Software Engineering, 36(1), 20–36.

    Article  MATH  Google Scholar 

  • Munro, M. J. (2005). Product metrics for automatic identification of “Bad Smell” design problems in java source-code. In Proceedings of the 11th international software metrics symposium.

  • Palomba, F., Bavota, G., Di Penta, M., Oliveto, R., & De Lucia, A. (2014). Do they really smell bad? A study on developers’ perception of bad code smells. In Software maintenance and evolution (ICSME), 2014 IEEE international conference on (pp. 101–110). IEEE.

  • Palomba, F., Bavota, G., Di Penta, M., Oliveto, R., De Lucia, A., & Poshyvanyk, D. (2013). Detecting bad smells in source code using change history information. In Automated software engineering (ASE), 2013 IEEE/ACM 28th international conference on (pp. 268–278). IEEE.

  • Rasool, G., & Arshad, Z. (2015). A review of code smell mining techniques. Journal of Software: Evolution and Process, 27(11), 867–895.

    Google Scholar 

  • Sahin, D., Kessentini, M., Bechikh, S., & Deb, K. (2014). Code-smell detection as a bilevel problem. ACM Transactions on Software Engineering and Methodology (TOSEM), 24(1), 6.

    Article  Google Scholar 

  • Salehie, M., Li, S., & Tahvildari, L. (2006). A metric-based heuristic framework to detect object-oriented design flaws. In Proceedings of the 14th IEEE ICPC’06.

  • Sjøberg, D. I. K., Yamashita, A. F., Anda, B. C. D., Mockus, A., & Dybå, T. (2013). Quantifying the effect of code smells on maintenance effort. IEEE Transactions on Software Engineering, 39(8), 1144–1156.

    Article  Google Scholar 

  • Travassos, G., Shull, F., Fredericks, M., & Basili, V. R. (1999). Detecting defects in object-oriented designs: Using reading techniques to increase software quality. In Proceedings of the 14th conference on object-oriented programming, systems, languages, and applications (pp. 47–56). New York: ACM Press.

  • Tufano, M., Palomba, F., Bavota, G., Oliveto, R., Di Penta, M., De Lucia, A., et al. (2015). When and why your code starts to smell bad. In ICSE.

  • Van Emden, V. & Moonen, L. (2002). Java quality assurance by detecting code smells. In Proceedings of the ninth working conference on reverse engineering (WCRE’02). IEEE computer society, Washington, DC, USA (p. 97).

  • Vidal, S. A., Marcos, C., & Díaz-Pace, J. A. (2014). An approach to prioritize code smells for refactoring. In Automated Software Engineering (pp. 1–32).

  • Yamashita, A. F. & Moonen, L. (2012) Do code smells reflect important maintainability aspects? In ICSM, pp. 306–315.

  • Yamashita, A. F., & Moonen, L. (2013a). To what extent can maintenance problems be predicted by code smell detection? An empirical study. Information & Software Technology, 55(12), 2223–2242.

    Article  Google Scholar 

  • Yamashita, A. F., & Moonen, L. (2013b). To what extent can maintenance problems be predicted by code smell detection? An empirical study. Information and Software Technology, 55(12), 2223–2242.

    Article  Google Scholar 

  • Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transaction on Evolutionary Computation, 7(2), 117–132.

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Michigan, Dearborn, MI, USA

    Usman Mansoor, Marouane Kessentini & Bruce R. Maxim

  2. Michigan State University, East Lansing, MI, USA

    Kalyanmoy Deb

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  1. Usman Mansoor
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  2. Marouane Kessentini
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  3. Bruce R. Maxim
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  4. Kalyanmoy Deb
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Correspondence to Marouane Kessentini.

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Mansoor, U., Kessentini, M., Maxim, B.R. et al. Multi-objective code-smells detection using good and bad design examples. Software Qual J 25, 529–552 (2017). https://doi.org/10.1007/s11219-016-9309-7

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