Refactoring is a critical part of the software development lifecycle, and identifier renaming accounts for roughly 15% of all agentic refactoring work driven by large language models. Yet the dominant model families fit the task poorly. Autoregressive decoders generate left to right, and even with the fill-in-the-middle extension they resolve masked positions one at a time, so a renaming decision at one site cannot inform a decision at another. Identifier renaming, however, demands consistency across every affected site at once. Diffusion Large Language Models (dLLMs) generate by iteratively denoising a masked sequence under full bidirectional attention, with every prediction conditioned on every other. This matches what renaming needs: if a poorly named identifier is viewed as a small amount of semantic noise overlaid on correct code, then renaming becomes a targeted denoising task that can be solved jointly across all affected sites.

We instantiate this view as CoReFusion, the first systematic study of dLLMs on Java identifier renaming, and benchmark them against twelve decoder-only FIM-AR baselines and five encoder-decoder Seq2Seq baselines on the RefineID dataset. DreamCoder-7B and DiffuCoder-7B reach 33.2% and 31.1% Exact Match, beating the best non-dLLM model (CodeT5-large) by more than ten points while being roughly nine times smaller than the largest FIM-AR baseline. The advantage grows with the number of identifiers that must be renamed together: FIM-AR models win the single-site case, but dLLMs pull ahead as soon as the task involves more than one site. When the same dLLMs must instead find the positions on their own, Exact Match drops to about 3%, and most wrong predictions copy the lexical style of the surrounding code rather than improve on it. Probing the internal states of DiffuCoder-7B shows why: the signal that tells a bad name from a good one appears only in the last few layers and the last few denoising steps, after the unmasking schedule has already confirmed most of its predictions. Providing the rename positions as masks bypasses this timing problem, which is why dLLMs work as filling engines but not as standalone refactoring agents. ... Read more

After the emergence of BERT, Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities and have seen widespread adoption globally, particularly in the field of programming. However, current evaluations and benchmarks of LLMs on code primarily focus on English use cases. In this study, we assess the performance of LLMs in generating Chinese Java code comments through open coding. Our experiments highlight the prevalence of model-specific and semantic errors in generating Chinese code comments using LLMs, while also revealing a relative absence of grammatical issues due to the unique characteristics of the Chinese language. Additionally, we validated the potential for quantitatively analyzing semantic errors, especially Hallucinations, by examining the cosine similarity of word embeddings. Our findings propose an Error Taxonomy for evaluating LLMs on code in non-English scenarios and demonstrate the possibilities of using cosine similarity of word embeddings to judge the quality of code comment generation. ... Read more