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Systematic reviews (SR) are essential for evidence-based research, but their screening phase is highly time-consuming and labor-intensive. Large language models (LLMs) offer a promising opportunity to reduce this workload by assisting with article relevance classification. However, existing evaluation approaches often...
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...
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...
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