Large language models are becoming increasingly practical for translating code across programming languages, a process known as $transpiling$. Even though automated transpilation significantly boosts developer productivity, a key concern is whether the generated code is correct. Existing work initially used manually crafted test suites to test the translations of a small corpus of programs; these test suites were later automated. In contrast, we devise the first approach for automated, functional, property-based testing of code translation models. Our general, user-provided specifications about the transpiled code capture a range of properties, from purely syntactic to purely semantic ones. As shown by our experiments, this approach is very effective in detecting property violations in popular code translation models, and therefore, in evaluating model quality with respect to given properties. We also go a step further and explore the usage scenario where a user simply aims to obtain a correct translation of some code with respect to certain properties without necessarily being concerned about the overall quality of the model. To this purpose, we develop the first property-guided search procedure for code translation models, where a model is repeatedly queried with slightly different parameters to produce alternative and potentially more correct translations. Our results show that this search procedure helps to obtain significantly better code translations.

翻译：大型语言模型在跨编程语言翻译代码（即所谓的"转译"过程）中正变得日益实用。尽管自动转译显著提升了开发者的生产力，但一个关键问题在于生成的代码是否正确。现有工作最初使用手工构建的测试套件来测试少量程序集的翻译结果；这些测试套件后来实现了自动化。与此相反，我们提出了首个针对代码翻译模型的自动化、功能性、基于属性的测试方法。我们提供的通用用户自定义规范能够捕获从纯语法到纯语义等一系列属性。实验表明，该方法在检测流行代码翻译模型的属性违规方面非常有效，因此能够基于给定属性评估模型质量。我们更进一步探索了这样的使用场景：用户仅希望针对特定属性获得某些代码的正确翻译，而无需关心模型的整体质量。为此，我们开发了首个面向代码翻译模型的属性引导搜索过程，通过反复查询参数略有差异的模型来生成替代性且可能更正确的翻译。结果表明，该搜索过程有助于获得显著更优的代码翻译。