Journal of Electrical and Electronic Engineering

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Research of Automatic Scoring of Student Programs Based on Static Analysis

Received: 19 June 2018    Accepted:     Published: 20 June 2018
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Abstract

In the student programming examination, the program must be automatically evaluated. It can be not give a reasonable score to the wrong program by comparing the output results of the dynamic evaluation method. Only by using the static analysis of the program can it give more accurate results. In this paper, the ratio of the length of the program feature vector and the Token sequence are introduced in two static analysis algorithms of attribute count and the longest common subsequence, and the optimum weight of various algorithms is determined by experiments. The experimental results show that the score given by the algorithm is very close to the teacher's score, which proves that the algorithm is an effective automatic scoring method.

DOI 10.11648/j.jeee.20180602.13
Published in Journal of Electrical and Electronic Engineering (Volume 6, Issue 2, April 2018)
Page(s) 53-58
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Automatic Scoring, Static Analysis, Attribute Counting, Longest Common Subsequence

References
[1] Basit H A, Puglisi S J, Smyth W F, “Efficient token based clone detection with flexible tokenization,” Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, 2007[C]. Dubrovnik: ACM, 2007, pp. 513-516.
[2] YU Hai-ying, “Research and Implementation of Program Code Similarity Measurement”, Computer Engineering, 2010, 36(4), pp. 45.
[3] GU Ping, ZHANG Feng, ZHOU Hai-tao, “Method of Program Source Code Similarity Measurement, Computer Engineering, 2012, 38(6), pp. 37.
[4] CUI Shuning, WU Ning, YE Dan, “Grade C++ code judge with constructing abstract syntax tree model,” Journal of Computer Application, 2015, 35(S1), pp. 183.
[5] Wang Tiantian, “Research on Program Recognition Approach Based on Structural Semantic Similarity,” unpublished.
[6] ZHANG Li-ping, LIU Dong-sheng, LI Yan-chen, “AST- based code plagiarism detection method,” Application Research of Computer, 2011, 28(12), pp. 4616.
[7] Kim J, Choi H, Yun H, “Measuring Source Code Similarity by Finding Similar Subgraph with an Incremental Genetic Algorithm,” Proceedings of the Genetic and Evolutionary Computation Conference 2016, Denver: ACM, 2016, pp. 925-932.
[8] LIU Yun-long, “Token-based structured code matching homology detection technology,” Application Research of Computers, 2014, 31(6), pp. 1841.
[9] LIU Yun-long, “A Homology Detection Technology Based on Improved Edit Distance and LCS,” Journal of Beijing Institute of Technology, 2017, 37(2), pp. 168.
[10] ZHANG Peng, “Research and Realization of Recognition Method on C Program Similar code,” unpublished.
[11] Jones E L, “Metrics based plagiarism monitoring,” Proceedings of the sixth annual CCSC northeastern conference on the journal of computing in small colleges, 2001[C]. Middlebury: Consortium for Computing Sciences in Colleges, 2001, pp. 253-261.
[12] ZHANG Jiujie, WANG Chunhui, ZHANG Liping, “Clone code detection based on Levenshtein distance of token,” Journal of Computer Application, 2015, 35(12), pp. 3536.
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  • APA Style

    Dongmei Yan, Xiangyuan Qi, Wenyue Yang. (2018). Research of Automatic Scoring of Student Programs Based on Static Analysis. Journal of Electrical and Electronic Engineering, 6(2), 53-58. https://doi.org/10.11648/j.jeee.20180602.13

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    ACS Style

    Dongmei Yan; Xiangyuan Qi; Wenyue Yang. Research of Automatic Scoring of Student Programs Based on Static Analysis. J. Electr. Electron. Eng. 2018, 6(2), 53-58. doi: 10.11648/j.jeee.20180602.13

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    AMA Style

    Dongmei Yan, Xiangyuan Qi, Wenyue Yang. Research of Automatic Scoring of Student Programs Based on Static Analysis. J Electr Electron Eng. 2018;6(2):53-58. doi: 10.11648/j.jeee.20180602.13

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  • @article{10.11648/j.jeee.20180602.13,
      author = {Dongmei Yan and Xiangyuan Qi and Wenyue Yang},
      title = {Research of Automatic Scoring of Student Programs Based on Static Analysis},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {6},
      number = {2},
      pages = {53-58},
      doi = {10.11648/j.jeee.20180602.13},
      url = {https://doi.org/10.11648/j.jeee.20180602.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20180602.13},
      abstract = {In the student programming examination, the program must be automatically evaluated. It can be not give a reasonable score to the wrong program by comparing the output results of the dynamic evaluation method. Only by using the static analysis of the program can it give more accurate results. In this paper, the ratio of the length of the program feature vector and the Token sequence are introduced in two static analysis algorithms of attribute count and the longest common subsequence, and the optimum weight of various algorithms is determined by experiments. The experimental results show that the score given by the algorithm is very close to the teacher's score, which proves that the algorithm is an effective automatic scoring method.},
     year = {2018}
    }
    

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    T1  - Research of Automatic Scoring of Student Programs Based on Static Analysis
    AU  - Dongmei Yan
    AU  - Xiangyuan Qi
    AU  - Wenyue Yang
    Y1  - 2018/06/20
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    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
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    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20180602.13
    AB  - In the student programming examination, the program must be automatically evaluated. It can be not give a reasonable score to the wrong program by comparing the output results of the dynamic evaluation method. Only by using the static analysis of the program can it give more accurate results. In this paper, the ratio of the length of the program feature vector and the Token sequence are introduced in two static analysis algorithms of attribute count and the longest common subsequence, and the optimum weight of various algorithms is determined by experiments. The experimental results show that the score given by the algorithm is very close to the teacher's score, which proves that the algorithm is an effective automatic scoring method.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Information Science and Technology, Tianjin University of Finance Economics, Tianjin, China

  • Department of Information Science and Technology, Tianjin University of Finance Economics, Tianjin, China

  • Department of Information Science and Technology, Tianjin University of Finance Economics, Tianjin, China

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