Token-based Approach for Real-time Plagiarism Detection in Digital Designs

This Research to Practice Work in Progress Paper presents a token-based approach to detecting plagiarism in university courses with hardware programming assignments. Detecting plagiarism manually is a difficult and time-consuming work. In the last two decades, various of plagiarism detection tools have been developed. These techniques could be mainly divided into the following categories: Textual Match, Program Dependence Graph Comparison, Abstract Syntax Tree Analysis and Low-Level Form Code Comparison. Although there had been a lot of researches on detecting code clones in software programming languages (e.g. Basic, C/C++, Java, Python, etc.), research that focused on hardware description languages is still lacking. Based on the effective of the locality sensitive hash function (simhash), which was usually used in detecting near duplicates for web crawling, we proposed an improved real-time plagiarism detection approach for Verilog HDL (hardware description language) programming assignments. The core detecting steps are extracting weighted tokens from source code as high-dimensional feature, and mapping it to a f-bit fingerprints with simhash technique. On account of the syntax characteristics of Verilog HDL, a token extraction strategy was designed to maximize the valid information that a fixed length hash value could represent. Experiments over real course data sets were conducted to evaluate the performance of token-based approach comparing with an existing plagiarism detection tool (Moss). The result shows that our token-based approach does qualify the plagiarism detecting job for both online-query and batch-query in digital designs. Furthermore, token-based plagiarism detection approach could enable conduct incremental plagiarism detection for a single submission without excessive overhead. Finally, we also give a discussion of current way limitations and future research directions.