Development and Benchmarking of Multilingual Code Clone Detector

• URL: http://arxiv.org/abs/2409.06176v2
• Date: Tue, 17 Sep 2024 06:31:20 GMT
• Title: Development and Benchmarking of Multilingual Code Clone Detector
• Authors: Wenqing Zhu, Norihiro Yoshida, Toshihiro Kamiya, Eunjong Choi, Hiroaki Takada,
• Abstract summary: multilingual code clone detectors make it easier to add new language support by providing syntax information of the target language only. We propose a multilingual code block extraction method based on ANTLR generation and implement a multilingual code clone detector (MSCCD) Compared to ten state-of-the-art detectors, MSCCD performs at an average level while it also supports a significantly larger number of languages.
• Score: 2.253851493296371
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The diversity of programming languages is growing, making the language extensibility of code clone detectors crucial. However, this is challenging for most existing clone detection detectors because the source code handler needs modifications, which require specialist-level knowledge of the targeted language and is time-consuming. Multilingual code clone detectors make it easier to add new language support by providing syntax information of the target language only. To address the shortcomings of existing multilingual detectors for language scalability and detection performance, we propose a multilingual code block extraction method based on ANTLR parser generation, and implement a multilingual code clone detector (MSCCD), which supports the most significant number of languages currently available and has the ability to detect Type-3 code clones. We follow the methodology of previous studies to evaluate the detection performance of the Java language. Compared to ten state-of-the-art detectors, MSCCD performs at an average level while it also supports a significantly larger number of languages. Furthermore, we propose the first multilingual syntactic code clone evaluation benchmark based on the CodeNet database. Our results reveal that even when applying the same detection approach, performance can vary markedly depending on the language of the source code under investigation. Overall, MSCCD is the most balanced one among the evaluated tools when considering detection performance and language extensibility.

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