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An Approach for Semantically-Enriched Recommendation of Refactorings Based on the Incidence of Code Smells

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Book cover Enterprise Information Systems (ICEIS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 321))

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Abstract

Code smells are symptoms of bad decisions on the design and development of software. The occurrence of code smells in software can lead to costly consequences. Refactorings are considered adequate resources when it comes to reducing or removing the undesirable effects of smells in software. Ontologies and semantics can play a substantial role in reducing the interpretation burden of software engineers as they have to decide about adequate refactorings to mitigate the impact of smells. However, related work has given little attention to associating the recommendation of refactorings with the use of ontologies and semantics. Developers can benefit from the combination of code smells detection with a semantically-oriented approach for recommendation of refactorings. To make this possible, we expand the application of our previous ontology, ONTOlogy for Code smEll ANalysis (ONTOCEAN), to combine it with a new one, Ontology for SOftware REfactoring (OSORE). We also introduce a new tool, our REfactoring REcommender SYStem (RESYS) which is capable of binding our two ontologies. As a result, refactorings are automatically chosen and semantically linked to their respective code smells. We also conducted a preliminary evaluation of our approach in a real usage scenario with four open-source software projects.

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Notes

  1. 1.

    ‘DC’ is used as an example to illustrate the representation of refactorings, but OSORE contains other ones. The full schema of OSORE’s ontological classes and instances can be found at the end of this paper.

  2. 2.

    We used OCEAN in our previous work to automate the detection of code smells.

  3. 3.

    http://owlapi.sourceforge.net/.

  4. 4.

    https://jersey.github.io/.

  5. 5.

    Assertional Box.

  6. 6.

    Terminological Box.

  7. 7.

    https://github.com/junit-team.

  8. 8.

    https://github.com/apache/log4j.

  9. 9.

    https://github.com/google/guava.

  10. 10.

    https://github.com/dbfit.

  11. 11.

    We called such developers ‘code smells propagators’.

  12. 12.

    The name of the developer was blurred out for privacy.

  13. 13.

    https://www.w3.org/TR/sparql11-overview/.

  14. 14.

    All prefixes of referenced ontologies has been shortened to better fit in article’s page.

  15. 15.

    http://protege.stanford.edu/.

  16. 16.

    Details about how to use our ontologies and tools can be found at: https://github.com/luispscarvalho/resys/wiki.

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Author information

Authors and Affiliations

  1. Federal Institute of Bahia, 3150 - Zabele, Vitoria da Conquista, Bahia, Brazil

    Luis Paulo da Silva Carvalho

  2. Federal Institute of Bahia, 39 - Canela, Av. Araújo Pinho, Salvador, Bahia, Brazil

    Renato Lima Novais

  3. Federal University of Bahia, Av. Ademar de Barros, s/n, Ondina, Salvador, Bahia, Brazil

    Laís do Nascimento Salvador & Manoel Gomes de Mendonça Neto

Authors
  1. Luis Paulo da Silva Carvalho
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  2. Renato Lima Novais
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  3. Laís do Nascimento Salvador
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  4. Manoel Gomes de Mendonça Neto
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Corresponding authors

Correspondence to Luis Paulo da Silva Carvalho or Renato Lima Novais .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. MODESTE/ESEO, Angers, France

    Olivier Camp

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Poland

    Joaquim Filipe

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Carvalho, L.P.d.S., Novais, R.L., Salvador, L.d.N., Neto, M.G.d.M. (2018). An Approach for Semantically-Enriched Recommendation of Refactorings Based on the Incidence of Code Smells. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-93375-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-93375-7_15

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