Stack Overflow is one of the most popular programming communities where developers can seek help for their encountered problems. Nevertheless, if inexperienced developers fail to describe their problems clearly, it is hard for them to attract sufficient attention and get the anticipated answers. We propose M$_3$NSCT5, a novel approach to automatically generate multiple post titles from the given code snippets. Developers may use the generated titles to find closely related posts and complete their problem descriptions. M$_3$NSCT5 employs the CodeT5 backbone, which is a pre-trained Transformer model having an excellent language understanding and generation ability. To alleviate the ambiguity issue that the same code snippets could be aligned with different titles under varying contexts, we propose the maximal marginal multiple nucleus sampling strategy to generate multiple high-quality and diverse title candidates at a time for the developers to choose from. We build a large-scale dataset with 890,000 question posts covering eight programming languages to validate the effectiveness of M$_3$NSCT5. The automatic evaluation results on the BLEU and ROUGE metrics demonstrate the superiority of M$_3$NSCT5 over six state-of-the-art baseline models. Moreover, a human evaluation with trustworthy results also demonstrates the great potential of our approach for real-world application.

提出了一种名为M$_3$NSCT5的方法，利用CodeT5 Transformer模型生成与编码片段相关的多个问题标题，以提高程序员提问的精度并加快问题解决速度，评估结果表明该方法的效果明显优于其他六种基准模型，同时经人工评估也证明了其在实际应用中的潜力。

使用多重采样增强Transformer生成多样化的Stack Overflow帖子标题