[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Investigating Freshmen Students’ Coding Standards Challenges Using NLP Techniques

  • Conference paper
  • First Online:
Information, Communication and Computing Technology (ICICCT 2024)

Abstract

This study investigates the potential use of Natural Language Processing (NLP) techniques to analyze coding standards violations within the context of an introductory programming course. In particular, the study evaluates the effectiveness of various advanced text embedding techniques, including Bag of Words (BOW), Doc2Vec, and BERT, in clustering coding standards violations. This study aims to determine which text embedding techniques contribute to the most accurate clustering of errors. Our findings highlight the superiority of Doc2Vec embeddings in effectively clustering related errors compared to the alternative techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 79.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 54.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://checkstyle.org/checks/whitespace/whitespacearound.html.

  2. 2.

    https://www.oracle.com/java/technologies/javase/codeconventions-whitespace.html.

  3. 3.

    https://pmd.github.io/pmd/pmd_rules_java_bestpractices.html#systemprintln.

  4. 4.

    https://hugginface.com/bert-base-uncased.

References

  1. Li, X., Prasad, C.: Effectively teaching coding standards in programming. In: Proceedings of the 6th Conference on Information Technology Education. SIGITE 2005, pp. 239–244. Association for Computing Machinery, New York, NY, USA (2005). https://doi.org/10.1145/1095714.1095770

  2. Chen, H.-M., Chen, W.-H., Lee, C.-C.: An automated assessment system for analysis of coding convention violations in java programming assignments. J. Inf. Sci. Eng. 34, 1203–1221 (2018)

    Google Scholar 

  3. Hofbauer, M., Bachhuber, C., Kuhn, C., Steinbach, E.: Teaching software engineering as programming over time. In: 2022 IEEE/ACM 4th International Workshop on Software Engineering Education for the Next Generation (SEENG), pp. 51–58 (2022). https://doi.org/10.1145/3528231.3528353

  4. Karnalim, O., Simon, Chivers, W.: Work-in-progress: code quality issues of computing undergraduates. In: 2022 IEEE Global Engineering Education Conference (EDUCON), pp. 1734–1736 (2022). https://doi.org/10.1109/EDUCON52537.2022.9766807

  5. Karnalim, O., Simon: Promoting code quality via automated feedback on student submissions. In: 2021 IEEE Frontiers in Education Conference (FIE), pp. 1–5 (2021). https://doi.org/10.1109/FIE49875.2021.9637193

  6. Albluwi, I., Salter, J.: Using static analysis tools for analyzing student behavior in an introductory programming course. Jordanian J. Comput. Inform. Technol. (JJCIT) 6(3), 215–233 (2020)

    Google Scholar 

  7. Checkstyle. https://checkstyle.sourceforge.io. Accessed 7 Jan 2024

  8. PMD. https://pmd.github.io. Accessed 7 Jan 2024

  9. He, J., Xu, L., Yan, M., Xia, X., Lei, Y.: Duplicate bug report detection using dual-channel convolutional neural networks. In: Proceedings of the 28th International Conference on Program Comprehension, pp. 117–127 (2020)

    Google Scholar 

  10. Imhmed, E., Ceh-Varela, E., Scott, K.: Identifying code quality issues for undergraduate students using static analysis and NLP. In: 2023 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE (2023)

    Google Scholar 

  11. Abubakar, H.D., Umar, M., Bakale, M.A.: Sentiment classification: Review of text vectorization methods: bag of words, Tf-Idf, Word2vec and Doc2vec. SLU J. Sci. Technol. 4(1 & 2), 27–33 (2022)

    Article  Google Scholar 

  12. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196. PMLR (2014)

    Google Scholar 

  13. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  14. Stegeman, M., Barendsen, E., Smetsers, S.: Designing a rubric for feedback on code quality in programming courses. In: Proceedings of the 16th Koli Calling International Conference on Computing Education Research. Koli Calling 2016, pp. 160–164. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2999541.2999555

  15. Edwards, S.H., Kandru, N., Rajagopal, M.B.M.: Investigating static analysis errors in student java programs. In: Proceedings of the 2017 ACM Conference on International Computing Education Research. ICER 2017, pp. 65–73. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3105726.3106182

  16. Oskouei, E.H., Kalıpsız, O.: Comparing bug finding tools for java open source software (2018)

    Google Scholar 

  17. Wang, J., Dong, Y.: Measurement of text similarity: a survey. Information 11(9), 421 (2020)

    Article  Google Scholar 

  18. Han, M., Zhang, X., Yuan, X., Jiang, J., Yun, W., Gao, C.: A survey on the techniques, applications, and performance of short text semantic similarity. Concurr. Comput. Pract. Exp. 33(5), 5971 (2021)

    Article  Google Scholar 

  19. Prakoso, D.W., Abdi, A., Amrit, C.: Short text similarity measurement methods: a review. Soft. Comput. 25, 4699–4723 (2021)

    Article  Google Scholar 

  20. Selva Birunda, S., Kanniga Devi, R.: A review on word embedding techniques for text classification. In: Raj, J.S., Iliyasu, A.M., Bestak, R., Baig, Z.A. (eds.) Innovative Data Communication Technologies and Application. LNDECT, vol. 59, pp. 267–281. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9651-3_23

    Chapter  Google Scholar 

  21. Ceh-Varela, E., Imhmed, E.: Uncovering water research with natural language processing. In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 983–984 (2023). https://doi.org/10.1109/COMPSAC57700.2023.00138

  22. Mitra, B., Craswell, N.: Neural text embeddings for information retrieval. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 813–814 (2017)

    Google Scholar 

  23. Greenacre, M., Groenen, P.J., Hastie, T., d’Enza, A.I., Markos, A., Tuzhilina, E.: Principal component analysis. Nat. Rev. Methods Primers 2(1), 100 (2022)

    Article  Google Scholar 

  24. Sinaga, K.P., Yang, M.-S.: Unsupervised k-means clustering algorithm. IEEE Access 8, 80716–80727 (2020)

    Article  Google Scholar 

  25. Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 747–748. IEEE (2020)

    Google Scholar 

  26. Imhmed, E., Cook, J., Badawy, A.-H.: Evaluation of a novel scratchpad memory through compiler supported simulation. In: 2022 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–7. IEEE (2022)

    Google Scholar 

  27. Imhmed, E.A.: Understanding performance of a novel local memory store design through compiler-driven simulation. PhD thesis, New Mexico State University (2022)

    Google Scholar 

  28. Akhila, C., Saleena, N.: Value based redundancy detection in SSA code. In: 2016 IEEE Annual India Conference (INDICON), pp. 1–5. IEEE (2016)

    Google Scholar 

  29. Zhang, M.: Detecting redundant operations with LLVM. http://james0zan.github.io/resource/GSoC15-Proposal-BloatDetection.pdf. Accessed 10 April 2024

  30. Abu-gellban, H., Zhuang, Y., Nguyen, L., Zhang, Z., Imhmed, E.: CSDLEEG: identifying confused students based on EEG using multi-view deep learning. In: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 1217–1222 (2022). https://doi.org/10.1109/COMPSAC54236.2022.00192

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Edgar Ceh-Varela .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ceh-Varela, E., Imhmed, E. (2025). Investigating Freshmen Students’ Coding Standards Challenges Using NLP Techniques. In: Weber, GW., Martinez Trinidad, J.F., Sheng, M., Ramachand, R., Kharb, L., Chahal, D. (eds) Information, Communication and Computing Technology. ICICCT 2024. Communications in Computer and Information Science, vol 2131. Springer, Cham. https://doi.org/10.1007/978-3-031-72483-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72483-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72482-4

  • Online ISBN: 978-3-031-72483-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics