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Source Code Clone Detection Using Unsupervised Similarity Measures

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Software Quality as a Foundation for Security (SWQD 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 505))

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Abstract

Assessing similarity in source code has gained significant attention in recent years due to its importance in software engineering tasks such as clone detection and code search and recommendation. This work presents a comparative analysis of unsupervised similarity measures for identifying source code clone detection. The goal is to overview the current state-of-the-art techniques, their strengths, and weaknesses. To do that, we compile the existing unsupervised strategies and evaluate their performance on a benchmark dataset to guide software engineers in selecting appropriate methods for their specific use cases. The source code of this study is available at https://github.com/jorge-martinez-gil/codesim

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Notes

  1. 1.

    https://github.com/oscarkarnalim/sourcecodeplagiarismdataset.

  2. 2.

    https://github.com/jorge-martinez-gil/codesim.

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Acknowledgments

The author thanks all the anonymous reviewers for their help in improving the manuscript. The research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Labour and Economy (BMAW), and the State of Upper Austria in the frame of the SCCH competence center INTEGRATE [(FFG grant no. 892418)] in the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.

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Authors and Affiliations

  1. Software Competence Center Hagenberg GmbH, Softwarepark 32a, 4232, Hagenberg, Austria

    Jorge Martinez-Gil

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  1. Jorge Martinez-Gil
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Correspondence to Jorge Martinez-Gil .

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Editors and Affiliations

  1. fortiss GmbH, Munich, Germany

    Peter Bludau

  2. Software Competence Center Hagenberg GmbH, Hagenberg, Austria

    Rudolf Ramler

  3. Austrian Center for Digital Production (CDP), SBA Research gGmbH, Vienna, Austria

    Dietmar Winkler

  4. Software Quality Lab GmbH, Linz, Austria

    Johannes Bergsmann

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Martinez-Gil, J. (2024). Source Code Clone Detection Using Unsupervised Similarity Measures. In: Bludau, P., Ramler, R., Winkler, D., Bergsmann, J. (eds) Software Quality as a Foundation for Security. SWQD 2024. Lecture Notes in Business Information Processing, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-56281-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-56281-5_2

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  • Online ISBN: 978-3-031-56281-5

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