In today’s software industry, rapid development cycles and intense competition have led to the widespread reuse and sharing of source code. Within this context, some companies have engaged in unauthorized replication of competitors’ code or violated open-source licenses to accelerate product development, leading to serious intellectual property infringements and legal disputes.
As plagiarism techniques become increasingly sophisticated through methods such as code obfuscation, structural reordering, and function fragmentation traditional string-based detection methods are no longer sufficient to accurately identify illicit reuse. In the field of software plagiarism detection, manual judgment may be feasible for small-scale software; however, for large-scale software systems, it is practically impossible to determine plagiarism manually. Therefore, automated similarity analysis tools are essential for accurate and efficient plagiarism detection. This paper classifies software into three categories—document-based (text), database-driven, and machine code—based on their characteristics, and investigates appropriate similarity analysis tools tailored to each type.