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Measuring similarity between Karel programs using character and word n-grams

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Abstract

We present a method for measuring similarity between source codes. We approach this task from the machine learning perspective using character and word n-grams as features and examining different machine learning algorithms. Furthermore, we explore the contribution of the latent semantic analysis in this task. We developed a corpus in order to evaluate the proposed approach. The corpus consists of around 10,000 source codes written in the Karel programming language to solve 100 different tasks. The results show that the highest classification accuracy is achieved when using Support Vector Machines classifier, applying the latent semantic analysis, and selecting as features trigrams of words.

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Authors and Affiliations

  1. Instituto Politécnico Nacional (IPN), Center for Computing Research (CIC), Mexico City, Mexico

    G. Sidorov, M. Ibarra Romero & I. Markov

  2. Engineering Division, University of Guanajuato, Campus Irapuato-Salamanca, Guanajuato, Mexico

    R. Guzman-Cabrera

  3. Instituto Politécnico Nacional, School of Mechanical and Electrical Engineering (ESIME), Mexico City, Mexico

    L. Chanona-Hernández

  4. Polytechnic University of Queretaro, Queretaro, Mexico

    F. Velásquez

Authors
  1. G. Sidorov
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  2. M. Ibarra Romero
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  3. I. Markov
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  4. R. Guzman-Cabrera
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  5. L. Chanona-Hernández
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  6. F. Velásquez
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Corresponding authors

Correspondence to G. Sidorov, I. Markov, R. Guzman-Cabrera, L. Chanona-Hernández or F. Velásquez.

Additional information

Original Russian Text © G. Sidorov, M. Ibarra Romero, I. Markov, R. Guzman-Cabrera, L. Chanona-Hernández, F. Velásquez, 2017, published in Programmirovanie, 2017, Vol. 43, No. 1.

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Sidorov, G., Ibarra Romero, M., Markov, I. et al. Measuring similarity between Karel programs using character and word n-grams. Program Comput Soft 43, 47–50 (2017). https://doi.org/10.1134/S0361768817010066

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  • DOI: https://doi.org/10.1134/S0361768817010066

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