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Prioritizing code-smells correction tasks using chemical reaction optimization

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Abstract

The presence of code-smells increases significantly the cost of maintenance of systems and makes them difficult to change and evolve. To remove code-smells, refactoring operations are used to improve the design of a system by changing its internal structure without altering the external behavior. In large-scale systems, the number of code-smells to fix can be very large and not all of them can be fixed automatically. Thus, the prioritization of the list of code-smells is required based on different criteria such as the risk and importance of classes. However, most of the existing refactoring approaches treat the code-smells to fix with the same importance. In this paper, we propose an approach based on a chemical reaction optimization metaheuristic search to find the suitable refactoring solutions (i.e., sequence of refactoring operations) that maximize the number of fixed riskiest code-smells according to the maintainer’s preferences/criteria. We evaluate our approach on five medium- and large-sized open-source systems and seven types of code-smells. Our experimental results show the effectiveness of our approach compared to other existing approaches and three different others metaheuristic searches.

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Authors and Affiliations

  1. DIRO, GEODES Lab, University of Montreal, Montreal, QC, Canada

    Ali Ouni & Houari Sahraoui

  2. CIS, SBSE-Michigan Lab, University of Michigan, Michigan, MI, USA

    Ali Ouni, Marouane Kessentini & Slim Bechikh

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  1. Ali Ouni
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  2. Marouane Kessentini
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Ouni, A., Kessentini, M., Bechikh, S. et al. Prioritizing code-smells correction tasks using chemical reaction optimization. Software Qual J 23, 323–361 (2015). https://doi.org/10.1007/s11219-014-9233-7

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