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Identifying Textual Features of High-Quality Questions: An Empirical Study on Stack Overflow

机译：识别高质量问题的文本特征：堆栈溢出的实证研究

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Background: Stack Overflow (SO) is a programming-specific Q&A website that serves as a valuable repository of software engineering knowledge. For SO members, formulating a good question is the first step towards eliciting satisfactory responses. Aims: To guide SO members on how to make a good question, we conduct an empirical study using the publicly available Stack Overflow Data Dump for the period of 2008-2016. Method: We first choose 25 features along 5 dimensions to represent the textual characteristics that we are interested in. Making use of the Boruta algorithm, we then capture all features that are either strongly or weakly relevant to the question quality. Results: The results show that the number of tags and code snippets are the most discriminative features, whereas there is only a weak correlation between the question quality and the sentiment-related factors. Based on the empirical evidence, we provide useful and usable suggestions to SO members on how to optimize their questions. Conclusions: We consider that our findings will provide SO members with a better understanding of the patterns behind high-quality questions, this is to support effective and efficient utilization of Q&A websites as the ultimate goal.
机译：背景：堆栈溢出（SO）是一个特定于编程的问答网站，可作为有价值的软件工程知识库。对于SO成员而言，提出一个好的问题是获得令人满意的回应的第一步。目的：为指导SO成员如何提出一个好问题，我们使用公开可用的Stack Overflow Data Dump（2008年至2016年）进行了实证研究。方法：我们首先在5个维度上选择25个特征来表示我们感兴趣的文本特征。使用Boruta算法，然后捕获与问题质量相关性强或弱的所有特征。结果：结果表明，标签和代码段的数量是最具有区别性的特征，而问题质量和与情感相关的因素之间的相关性很弱。基于经验证据，我们为SO成员提供了有关如何优化其问题的有用且有用的建议。结论：我们认为我们的发现将使SO成员对高质量问题背后的模式有更好的理解，这是支持有效和有效利用问答网站的最终目标。

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