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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.

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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

Authors
  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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