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 requires 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. © 2024 The Author(s)

Self-Admitted Technical Debt (SATD) refers to nonoptimal software design or implementation that is acknowledged and explicitly documented in the code by developers. Detecting SATD and understanding its evolution can help developers better manage their development activities and monitor the software quality. In recent years, numerous approaches have been proposed to automatically identify SATD. However, these approaches still suffer from a high number of false positives (i.e., non-SATD comments being detected as SATD). To further advance this field, in this paper, we conduct an empirical study to evaluate the performance of the state-of-theart SATD detection tools and investigate the causes behind the false positives. By manually analyzing 135 false positive cases, we identify the main types of comments that are easily misclassified. To address this issue, we propose a new approach, CASTI, which integrates context information into CodeBERT, a pre-trained model for programming languages. Our evaluation demonstrates that CASTI can significantly reduce the false positives and that the context information does help improve the performance. © 2025 IEEE.

The prevalence and impact of code clones in software systems have been widely studied in the past few decades. However, the focus has primarily been on intra-project clones. Our work comprehensively investigates cross-project code clones in open-source software for Internet of Things (IoT) systems across multiple programming languages. This work addresses the prevalence of cross-project code clones in IoT systems and their impact on software maintainability. We collected 122 IoT system repositories in nine languages from GitHub and grouped them according to their primary functionality in IoT systems. We used MSCCD, a multilingual code clone detector to detect Type-3 code clones for each group. The results show that cross-project clones exist in more than 30% of the projects, particularly in communication-related functionalities. We tracked the historical evolution of these clones and classified them according to the revision history and changing trend of similarity. The results show that 95% cross-project clones are untouched. Moreover, clones with decreasing similarities were over 72%. Therefore, the same clone detector may no longer detect these clones. We also investigated whether these cross-project code clones lead to defect propagation by analyzing the commit message to determine the commits that fixed a defect. We identified nine defect propagation instances, of which seven remain unfixed. Our work contributes to understanding cross-project code clones, highlighting the importance of automated clone management tools for improving the quality and security of IoT system software to mitigate the risks associated with unresolved defects and inconsistencies in IoT software development. © 2013 IEEE.