Code style transformation models built on code Language Models (code LMs) have achieved remarkable success. However, they typically focus on basis style transformations, where the target style follows a single criterion, and often struggle with combination styles, where the target style involves multiple criteria. In practice, style guides encompass multiple criteria, making the lack of effective combination style transformation a major limitation to their real-world applicability.

In this paper, we propose Absent-Basis-Combination (abbreviated as ABC), a novel framework for code style transformation that significantly improves combination style transformation and overcomes the limitations of existing approaches. We implement four variants of ABC with parameter sizes of 0.5B, 1.3B, 1.5B, and 3B, demonstrating consistent superiority over existing approaches across all model sizes in both basis and combination style transformations. Specifically, ABC achieves performance gains of up to 86.70%, and remains superior even when baseline approaches use three times the parameters. Furthermore, to address the lack of high-quality datasets and evaluation metrics, we construct and release a new style transformation dataset, Basis & Combination Code Style (abbreviated as BCCStyle), and introduce Code Sequence, Syntactic, Semantic and Stylistic BLEU (abbreviated as CS4BLEU), a novel code similarity metric that surpasses existing metrics in accuracy and consistency.