Comparison of CRC16 and PNC16 Models to Identify Errors in Python

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.