Stack Overflow

Alexander Fischer · Master of Science in Software & Data Engineering

Marco Bedulli · Bachelor of Science in Informatics

When knowledgeable colleagues are not available, developers resort to offline and online resources, e.g. tutorials, mailing lists, and Q&A websites. These, however, need to be found, read, and understood, which takes its toll in terms of time and mental energy. A more immediate and accessible resource are video tutorials found on the web, which in recent years have seen a steep increase in popularity. Nonetheless, videos are an intrinsically noisy data source, and finding the right piece of information might be even more cumbersome than using the previously mentioned resources. We present CodeTube, an approach which mines video tutorials found on the web, and enables developers to query their contents. The video tutorials are split into coherent fragments, to return only fragments related to the query. These are complemented with information from additional sources, such as Stack Overflow discussions. The results of two studies to assess CodeTube indicate that video tutorials - if appropriately processed - represent a useful, yet still under-utilized source of information for software development.

Nowadays developers heavily rely on sources of informal documentation. Examples include Q&A forums, slides, or video tutorials, the latter being particularly useful to provide introductory notions for a piece of technology. The current practice is that developers have to browse sources individually, which in the case of video tutorials is cumbersome, as they are lengthy and cannot be searched based on their contents. We present CodeTube, a Web-based recommender system that analyzes the contents of video tutorials and is able to provide, given a query, cohesive and self-contained video fragments, along with links to relevant Stack Overflow discussions. CodeTube relies on a combination of textual analysis and image processing applied on video tutorial frames and speech transcripts to split videos into cohesive fragments, index them and identify related Stack Overflow discussions.

Stack Overflow is the de facto Question and Answer (Q&A) website for developers, and it has been used in many approaches by software engineering researchers to mine useful data. However, the contents of a Stack Overflow discussion are inherently heterogeneous, mixing natural language, source code, stack traces and configuration files in XML or JSON format. We constructed a full island grammar capable of modeling the set of 700,000 Stack Overflow discussions talking about Java, building a heterogeneous abstract syntax tree (H-AST) of each post (question, answer or comment) in a discussion. The resulting dataset models every Stack Overflow discussion, providing a full H-AST for each type of structured fragment (i.e., JSON, XML, Java, Stack traces), and complementing this information with a set of basic meta-information like term frequency to enable natural language analyses. Our dataset allows the end-user to perform combined analyses of the Stack Overflow by visiting the H-AST of a discussion.

Heterogeneous abstract syntax trees for Stack Overflow: a Scala development kit, a ready-made JSON dataset, and an island-parsing web service

Technical questions and answers (Q&A) services have become a valuable resource for developers. A prominent example of technical Q&A website is StackOverflow (SO), which relies on a growing community of more than two millions of users who actively contribute by asking questions and providing answers. To maintain the value of this resource, poor quality questions - among the more than 6,000 asked daily - have to be filtered out. Currently, poor quality questions are manually identified and reviewed by selected users in SO, this costs considerable time and effort. Automating the process would save time and unload the review queue, improving the efficiency of SO as a resource for developers. We present an approach to automate the classification of questions according to their quality. We present an empirical study that investigates how to model and predict the quality of a question by considering as features both the contents of a post (e.g., from simple textual features to more complex readability metrics) and community-related aspects (e.g., popularity of a user in the community). Our findings show that there is indeed the possibility of at least a partial automation of the costly SO review process.

Stack Overflow is a popular questions and answers (Q&A) website among software developers. It counts more than two millions of users who actively contribute by asking and answering thousands of questions daily. Identifying and reviewing low quality posts preserves the quality of site's contents and it is crucial to maintain a good user experience. In Stack Overflow the identification of poor quality posts is performed by selected users manually. The system also uses an automated identification system based on textual features. Low quality posts automatically enter a review queue maintained by experienced users. We present an approach to improve the automated system in use at Stack Overflow. It analyzes both the content of a post (e.g., simple textual features and complex readability metrics) and community-related aspects (e.g., popularity of a user in the community). Our approach reduces the size of the review queue effectively and removes misclassified good quality posts.