Comments play an important role in updating Stack Overflow (SO) posts. They are used to point out a problem (e.g., obsolete answer and buggy code) in a SO answer or ask for more details about a proposed answer. We refer to this type of comment as update request comments (URCs), which may trigger an update to the answer post and thus improve its quality. In this study, we manually analyze a set of 384 sampled SO answer posts and their associated 1,221 comments to investigate the prevalence of URCs and how URCs are addressed. We find that around half of the analyzed comments are URCs. While 55.3% of URCs are addressed within 24 hours, 36.5% of URCs remain unaddressed after a year. Moreover, we find that the current community-vote mechanism could not differentiate URCs from non-URCs. Thus many URCs might not be aware by users who can address the issue or improve the answer quality. As a first step to enhance the awareness of URCs and support future research on URCs, we investigate the feasibility of URC detection by proposing a set of features extracted from different aspects of SO comments and using them to build supervised classifiers that can automatically identify URCs. Our experiments on 377 and 289 comments posted on answers to JavaScript and Python questions show that the proposed URC classifier can achieve an accuracy of 90% and an AUC of 0.96, on average.

翻译：评论在更新Stack Overflow（SO）帖子中起着重要作用。它们被用来指出SO回答中的问题（例如过时的答案或存在缺陷的代码），或要求对已提供的答案进行更多细节补充。我们将此类评论称为更新请求评论（URC），它们可能触发回答帖的更新，从而提升其质量。本研究通过人工分析384个抽样SO回答帖及其关联的1,221条评论，探究URC的普遍性及其处理方式。研究发现，约半数分析评论属于URC。其中55.3%的URC在24小时内得到处理，而36.5%的URC在一年后仍未得到回应。此外，当前社区投票机制无法区分URC与非URC，导致许多URC可能未被能够解决问题或改进回答质量的用户察觉。为增强URC的可感知性并支持未来相关研究，我们首先探究URC检测的可行性：从SO评论的不同维度提取特征集，并构建监督分类器以自动识别URC。在针对JavaScript和Python问题回答的377条和289条评论实验中，所提出的URC分类器平均准确率可达90%，AUC值达0.96。