10.25673/120441">
Proceedings of International Conference on Applied Innovation in IT  ·  2025/06/27  ·  Vol. 13  ·  Issue 2  ·  pp. 227–233
Comparison of CRC16 and PNC16 Models to Identify Errors in Python
Odilzhan Turdiev, Masud Masharipov, Maad Mudher Khalil and Mohammed Sami Mohammed
📄 Download PDF DOI: <a href="http://dx.doi.org/10.25673/120441">10.25673/120441</a>
Abstract
In the modern world of programming, where reliability and security are critically important aspects, the detection and correction of errors becomes an integral part of software development. One of the methods for detecting errors is the use of error codes, such as Cyclic Redundancy Checks (CRC) and probabilistic number code (PNC). In this work, we compare these two models for detecting errors in the python programming language. The aim of the study is to investigate the efficiency and applicability of these models for detecting errors in data, including 6-bit errors. Full-fledged code examples are provided for each model. These models are involved to provide analyzation of its contribution and how it deals with errors, which ensures data integrity through the full process. In addition, the performances of CRC and PNC for 6-bits are included and studied for this purpose. Results showed that CRC16 provide better performances than PNC16. The high reliability of CRC16 is due to restrict mathematical operations that CRC16 followed to detect errors. While PNC16 introduced uncertainty and occasional failures in detecting errors for the same data that has been used with CRC16.
Keywords
Python Cyclic Redundancy Code Probability Code Number Error Detection.
References
  1. O.A. Turdiev, "A model for the formation of a probable number code based on stochastic calculations," Intelligent Technologies in Transport, no. 4, pp. 28-33, 2021.
  2. A.H. Saleh and M.S. Mohammed, "Enhancing Data Security through Hybrid Error Detection: Combining Cyclic Redundancy Check (CRC) and Checksum Techniques," International Journal of Electrical Engineering Research, Aug. 2024, [Online]. Available: https://doi.org/10.37391/IJEER.120312.
  3. A. Kotade and A. Nandgaonkar, "Cyclic Redundancy Check: A Novel Software Implementation for machine cycle optimization and analysis for deciding Low Peak to Average Power Ratio Discrete Sequences," in ICCASP/ICMMD-2016, Advances in Intelligent Systems Research, vol. 137, pp. 441-449, 2017.
  4. P.B. Viegas, A.G. de Castro, A.F. Lorenzon, F.D. Rossi, and M.C. Luizelli, "The Actual Cost of Programmable SmartNICs: Diving into the Existing Limits," in Advanced Information Networking and Applications: Proceedings of the 35th International Conference on Advanced Information Networking and Applications (AINA-2021), vol. 1, Springer International Publishing, Germany, 2021, pp. 181-194.
  5. R.N. Williams, Elementary Guide to CRC Algorithms of Error Detection, Aug. 19, 1993.
  6. S.S. Gorshe, "CRC-16 polynomials optimized for applications using self-synchronous scramblers," in Proc. IEEE International Conference on Communications, vol. 5, pp. 2791-2795, 2002, [Online]. Available: https://doi.org/10.1109/ICC.2002.997351.
  7. P. Pramod, R. Anantharaman, and A. Kotain, "FPGA implementation of single bit error correction using CRC," International Journal of Computer Applications, vol. 52, pp. 15-19, 2012, [Online]. Available: https://doi.org/10.5120/8238-1471.
  8. B. Kirocuhenassamy, A. Yessad, S. Jolivet, and V. Luengo, "Toward diagnosis of semantic errors in Python programming platforms for beginners," in Workshop RKDE - ECML PKDD Conference, Vilnius, Lithuania, 2025.
  9. H. Campbell, A. Hindle, and J. Amaral, "Error location in Python: where the mutants hide," 2015, [Online]. Available: https://doi.org/10.7287/peerj.preprints.1132v1.
  10. D. Robinson, N. Ernst, E. Vargas, and M.-A. Storey, "Error Identification Strategies for Python Jupyter Notebooks," 2022, [Online]. Available: https://doi.org/10.48550/arXiv.2203.16653.
  11. M. Arutunian, S. Sargsyan, M. Mehrabyan, L. Bareghamyan, and H. Aslanyan, "Automatic Recognition and Replacement of Cyclic Redundancy Checks for Program Optimization," IEEE Access, pp. 1-1, 2024, [Online]. Available: https://doi.org/10.1109/ACCESS.2024.3518953.
  12. X. Dong and Y. He, "CRC Algorithm for Embedded System Based on Table Lookup Method," Microprocessors and Microsystems, vol. 74, p. 103049, 2020, [Online]. Available: https://doi.org/10.1016/j.micpro.2020.103049.
  13. J. Ray, "Review of Understanding Checksums and Cyclic Redundancy Checks —Philip Koopman (Boca Raton, FL, USA: CRC Press, 2024)," IEEE Reliability Magazine, pp. 1-2, 2024, [Online]. Available: https://doi.org/10.1109/MRL.2024.3389635.
  14. A. Akagic and H. Amano, "A study of adaptable co-processors for Cyclic Redundancy Check on an FPGA," in Proc. International Conference on Field-Programmable Technology (FPT), pp. 119-124, 2012, [Online]. Available: https://doi.org/10.1109/FPT.2012.6412122.
  15. J. Wan, B. Chen, and S. Wang, Smart Manufacturing Factory: Artificial-Intelligence-Driven Customized Manufacturing, CRC Press, 2023.

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