  With the involvement of multiple programming languages in modern software
development, cross-lingual code clone detection has gained traction with the
software engineering community. Numerous studies have explored this topic,
proposing various promising approaches. Inspired by the significant advances in
machine learning in recent years, particularly Large Language Models (LLMs),
which have demonstrated their ability to tackle various tasks, this paper
revisits cross-lingual code clone detection.
  We investigate the capabilities of four (04) LLMs and eight (08) prompts for
the identification of cross-lingual code clones. Additionally, we evaluate a
pre-trained embedding model to assess the effectiveness of the generated
representations for classifying clone and non-clone pairs. Both studies (based
on LLMs and Embedding models) are evaluated using two widely used cross-lingual
datasets, XLCoST and CodeNet. Our results show that LLMs can achieve high F1
scores, up to 0.98, for straightforward programming examples (e.g., from
XLCoST). However, they not only perform less well on programs associated with
complex programming challenges but also do not necessarily understand the
meaning of code clones in a cross-lingual setting. We show that embedding
models used to represent code fragments from different programming languages in
the same representation space enable the training of a basic classifier that
outperforms all LLMs by ~2 and ~24 percentage points on the XLCoST and CodeNet
datasets, respectively. This finding suggests that, despite the apparent
capabilities of LLMs, embeddings provided by embedding models offer suitable
representations to achieve state-of-the-art performance in cross-lingual code
clone detection.
