Stack Overflow is the most popular CQA for programmers on the web with 2.05M users, 5.1M questions and 9.4M answers. Stack Overflow has explicit, detailed guidelines on how to post questions and an ebullient moderation community. Despite these precise communications and safeguards, questions posted on Stack Overflow can be extremely off topic or very poor in quality. Such questions can be deleted from Stack Overflow at the discretion of experienced community members and moderators. We present the first study of deleted questions on Stack Overflow. We divide our study into two parts (i) Characterization of deleted questions over approx. 5 years (2008-2013) of data, (ii) Prediction of deletion at the time of question creation. Our characterization study reveals multiple insights on question deletion phenomena. We observe a significant increase in the number of deleted questions over time. We find that it takes substantial time to vote a question to be deleted but once voted, the community takes swift action. We also see that question authors delete their questions to salvage reputation points. We notice some instances of accidental deletion of good quality questions but such questions are voted back to be undeleted quickly. We discover a pyramidal structure of question quality on Stack Overflow and find that deleted questions lie at the bottom (lowest quality) of the pyramid. We also build a predictive model to detect the deletion of question at the creation time. We experiment with 47 features based on User Profile, Community Generated, Question Content and Syntactic style and report an accuracy of 66 . Our feature analysis reveals that all four categories of features are important for the prediction task. Our findings reveal important suggestions for content quality maintenance on community based question answering websites.
Nasehi analyze questions on Stack Overflow to understand the quality of a code example @cite_6 . They find nine attributes of good questions like concise code, links to extra resources and inline documentation. Wang and Godfrey analyze iOS and Android developer questions on Stack Overflow to detect API usage obstacles @cite_22 . They used topic models to find a set API classes on iOS and Android documentation which were difficult for developers to understand. Asaduzzaman analyze unanswered questions on Stack Overflow and use a machine learning classifier to predict such questions @cite_11 . They observe certain characteristics of unanswered questions which include vagueness, homework questions etc. Allamanis and Sutton perform a topic modeling analysis on Stack Overflow questions to combine topics, types and code @cite_19 . They find that programming languages are a mixture of concepts and questions on Stack Overflow are concerned with the code example rather than the application domain. In contrast to the aforementioned work, our work specifically focuses on quality of content on Stack Overflow.
@cite_19 @cite_22 @cite_6 @cite_11
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Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30 improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public. StackOverflow provides a popular platform where developers post and answer questions. Recently, manually label 385 questions in StackOverflow and group them into 10 categories based on their contents. They also analyze how tags are used in StackOverflow. In this study, we extend their work to obtain a deeper understanding on how developers interact with one another on such a question and answer web site. First, we analyze the distributions of developers who ask and answer questions. We also investigate if there is a segregation of the StackOverflow community into questioners and answerers. We also perform automated text mining to find the various kinds of topics asked by developers. We use Latent Dirichlet Allocation (LDA), a well known topic modeling approach, to analyze the contents of tens of thousands of questions and answers, and produce five topics. Our topic modeling strategy provides an alternative perspective different from that of for categorizing StackOverflow questions. Each question can now be categorized into several topics with different probabilities, and the learned topic model could automatically assign a new question to several categories with varying probabilities. Last but not least, we show the distributions of questions and developers belonging to various topics generated by LDA. Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as API documentation or Stack Overflow. We present an approach to automatically augment API documentation with "insight sentences" from Stack Overflow -- sentences that are related to a particular API type and that provide insight not contained in the API documentation of that type. Based on a development set of 1,574 sentences, we compare the performance of two state-of-the-art summarization techniques as well as a pattern-based approach for insight sentence extraction. We then present SISE, a novel machine learning based approach that uses as features the sentences themselves, their formatting, their question, their answer, and their authors as well as part-of-speech tags and the similarity of a sentence to the corresponding API documentation. With SISE, we were able to achieve a precision of 0.64 and a coverage of 0.7 on the development set. In a comparative study with eight software developers, we found that SISE resulted in the highest number of sentences that were considered to add useful information not found in the API documentation. These results indicate that taking into account the meta data available on Stack Overflow as well as part-of-speech tags can significantly improve unsupervised extraction approaches when applied to Stack Overflow data. Stack Overflow is the most popular Community based Question Answering (CQA) website for programmers on the web with 2.05M users, 5.1M questions and 9.4M answers. Stack Overflow has explicit, detailed guidelines on how to post questions and an ebullient moderation community. Despite these precise communications and safeguards, questions posted on Stack Overflow can be extremely off topic or very poor in quality. Such questions can be deleted from Stack Overflow at the discretion of experienced community members and moderators. We present the first study of deleted questions on Stack Overflow. We divide our study into two parts - (i) Characterization of deleted questions over 5 years (2008-2013) of data, (ii) Prediction of deletion at the time of question creation. Our characterization study reveals multiple insights on question deletion phenomena. We find that it takes substantial time to vote a question to be deleted but once voted, the community takes swift action. We also see that question authors delete their questions to salvage reputation points. We notice some instances of accidental deletion of good quality questions but such questions are voted back to be undeleted quickly. We discover a pyramidal structure of question quality on Stack Overflow and find that deleted questions lie at the bottom (lowest quality) of the pyramid. We also build a predictive model to detect the deletion of question at the creation time. We experiment with 47 features -- based on User Profile, Community Generated, Question Content and Syntactic style -- and report an accuracy of 66 . Our findings reveal important suggestions for content quality maintenance on community based question answering websites. To the best of our knowledge, this is the first large scale study on poor quality (deleted) questions on Stack Overflow.