Detecting Type-IV code clones, functionally equivalent fragments with different syntax, remains a major challenge for quality assurance. Existing datasets are limited in supporting semantic clone detection due to class imbalance, lack of verified functional equivalence, and data redundancy. We present an automated appr...
Luciano Marchezan, Eugene Syriani, Kévin Delcourt et al.· Proceedings of the ACM on So...· 1 citation
LWVIC4Code is proposed, a non-contrastive representation learning approach specifically designed for Type-IV clone detection that achieves competitive or superior performance without negative samples, benefits from layer-wise supervision, and generalizes effectively from Python to other languages, particularly Java and...
Luciano Marchezan, Kévin Delcourt, Eugene Syriani et al.· 0 citations
This paper proposes an automated approach to extract domain models from source code using lightweight, locally deployable LLMs and achieves high F1-scores on a dataset of ten projects, each comprising a curated domain model and its corresponding implementation, while remaining fully executable on locally deployable LLM...
Kévin Delcourt, Meriem Ben Chaaben, Abdelhamid Rouatbi et al.· Proceedings of the ACM/IEEE...· 1 citation
This paper proposes an automated approach to extract domain models from source code using lightweight, locally deployable LLMs and achieves high F1-scores on a dataset of ten projects, each comprising a curated domain model and its corresponding implementation, while remaining fully executable on locally deployable LLM...
Alessandra Mancas, Mounir Ammam, Hyacinth Ali et al.· 1 citation
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