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QOSCR: Quantification of Source Code Resemblance

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International Conference on Artificial Intelligence and Sustainable Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 837))

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Abstract

Discovering coping is a serious problem at dissimilar level and had become a serious issue for intellectuals. Finding out similarity in code that may be in same programming languages or converted into different programing language increases the problem complexity, and this problem can be faced in different fields like software development, educational institute, etc. For finding out plagiarism among programming codes, in this paper, we developed the QOSCR software. With the help of this software, we can find out the likeness among two code at procedure’s level. By using this software, reviewers of programming language can give the results to the scholars in a task of software design topic.

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Acknowledgements

We wish to thank all the teachers and software industries around the countries who actively contributed in this (QOSCR) tool.

Author information

Authors and Affiliations

  1. Computer Engineering and Applications, GLA University, Mathura, U.P., 281406, India

    Mayank Agrawal

Authors
  1. Mayank Agrawal
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Correspondence to Mayank Agrawal .

Editor information

Editors and Affiliations

  1. National Institute of Technology Durgapur, Durgapur, West Bengal, India

    Goutam Sanyal

  2. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Carlos M. Travieso-González

  3. Department of Computer Science and Engineering, G. L. Bajaj Institute of Technology and Management, Greater Noida, India

    Shashank Awasthi

  4. School of Engineering, Polytechnic of Porto, University of Porto, Porto, Portugal

    Carla M.A. Pinto

  5. Department of Planning and Development, National Institute of Technology Goa, Goa, India

    B. R. Purushothama

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Agrawal, M. (2022). QOSCR: Quantification of Source Code Resemblance. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M., Purushothama, B.R. (eds) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-16-8546-0_40

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