QOSCR: Quantification of Source Code Resemblance

Mayank Agrawal

doi:10.1007/978-981-16-8546-0_40

QOSCR: Quantification of Source Code Resemblance

Mayank Agrawal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020
Editors	Goutam Sanyal, Carlos M. Travieso-González, Shashank Awasthi, Carla M. Pinto, B. R. Purushothama
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	487-500
Number of pages	14
ISBN (Print)	9789811685453
DOIs	https://doi.org/10.1007/978-981-16-8546-0_40
State	Published - 1 Jan 2022
Externally published	Yes
Event	International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020 - Goa, India Duration: 27 Nov 2020 → 29 Nov 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	837
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020
Country/Territory	India
City	Goa
Period	27/11/20 → 29/11/20

Keywords

Plagiarism identification tool
Similarity detection
Source code plagiarism

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-16-8546-0_40

Cite this

Agrawal, M. (2022). QOSCR: Quantification of Source Code Resemblance. In G. Sanyal, C. M. Travieso-González, S. Awasthi, C. M. Pinto, & B. R. Purushothama (Eds.), International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020 (pp. 487-500). (Lecture Notes in Electrical Engineering; Vol. 837). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-8546-0_40

Agrawal, Mayank. / QOSCR : Quantification of Source Code Resemblance. International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020. editor / Goutam Sanyal ; Carlos M. Travieso-González ; Shashank Awasthi ; Carla M. Pinto ; B. R. Purushothama. Springer Science and Business Media Deutschland GmbH, 2022. pp. 487-500 (Lecture Notes in Electrical Engineering).

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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{\textquoteright}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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author = "Mayank Agrawal",

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Agrawal, M 2022, QOSCR: Quantification of Source Code Resemblance. in G Sanyal, CM Travieso-González, S Awasthi, CM Pinto & BR Purushothama (eds), International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020. Lecture Notes in Electrical Engineering, vol. 837, Springer Science and Business Media Deutschland GmbH, pp. 487-500, International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020, Goa, India, 27/11/20. https://doi.org/10.1007/978-981-16-8546-0_40

QOSCR: Quantification of Source Code Resemblance. / Agrawal, Mayank.
International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020. ed. / Goutam Sanyal; Carlos M. Travieso-González; Shashank Awasthi; Carla M. Pinto; B. R. Purushothama. Springer Science and Business Media Deutschland GmbH, 2022. p. 487-500 (Lecture Notes in Electrical Engineering; Vol. 837).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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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Agrawal M. QOSCR: Quantification of Source Code Resemblance. In Sanyal G, Travieso-González CM, Awasthi S, Pinto CM, Purushothama BR, editors, International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020. Springer Science and Business Media Deutschland GmbH. 2022. p. 487-500. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-8546-0_40

QOSCR: Quantification of Source Code Resemblance

Abstract

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Keywords

ASJC Scopus subject areas

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