Luca Ponzanelli

Mining StackOverflow to turn the IDE into a self-confident programming prompter

Developers often require knowledge beyond the one they possess, which often boils down to consulting sources of information like Application Programming Interfaces (API) documentation, forums, Q&A websites, etc. Knowing what to search for and how is non- trivial, and developers spend time and energy to formulate their problems as queries and to peruse and process the results. We propose a novel approach that, given a context in the IDE, automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance, and, if a given confidence threshold is surpassed, notifies the developer about the available help. We have implemented our approach in Prompter, an Eclipse plug-in. Prompter has been evaluated through two studies. The first was aimed at evaluating the devised ranking model, while the second was conducted to evaluate the usefulness of Prompter.

Seahawk: stack overflow in the IDE

Services, such as Stack Overflow, offer a web platform to programmers for discussing technical issues, in form of Question and Answers (Q&A). Since Q&A services store the discussions, the generated “crowd knowledge” can be accessed and consumed by a large audience for a long time. Nevertheless, Q&A services are detached from the development environments used by programmers: Developers have to tap into this crowd knowledge through web browsers and cannot smoothly integrate it into their workflow. This situation hinders part of the benefits of Q&A services. To better leverage the crowd knowledge of Q&A services, we created Seahawk, an Eclipse plugin that supports an integrated and largely automated approach to assist programmers using Stack Overflow. Seahawk formulates queries automatically from the active context in the IDE, presents a ranked and interactive list of results, lets users import code samples in discussions through drag & drop and link Stack Overflow discussions and source code persistently as a support for team work. Video Demo URL: http://youtu.be/DkqhiU9FYPI.

Harnessing stack overflow for the IDE

Developers often consult online tutorials and message boards to find solutions to their programming issues. Among the many online resources, Question & Answer websites are gaining popularity. This is no wonder if we consider a case like Stack Overflow, where more than 92% questions on expert topics are answered in a median time of 11 minutes. This new resource has scarcely been acknowledged by any Integrated Development Environment (IDE): Even though developers spend a large part of their working time in IDEs, and the usage of Q&A services has dramatically increased, developers can only use such resources using external applications. We introduce Seahawk, an Eclipse plugin to integrate Stack Overflow crowd knowledge in the IDE. It allows developers to seamlessly access Stack Overflow data, thus obtaining answers without switching the context. We present our preliminary work on Seahawk: It allows users to (1) retrieve Q&A from Stack Overflow, (2) link relevant discussions to any source code in Eclipse, and (3) attach explanative comments to the links.

Improving Low Quality Stack Overflow Post Detection

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 sites 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.

Understanding and Classifying the Quality of Technical Forum Questions

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.

Too long; didn't watch!: extracting relevant fragments from software development video tutorials

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.

Prompter: A Self-Confident Recommender System

Developers often consult different sources of information like Application Programming Interfaces (API) documentation, forums, Q&A websites, etc. With the aim of gathering additional knowledge for the programming task at hand. The process of searching and identifying valuable pieces of information requires developers to spend time and energy in formulating the right queries, assessing the returned results, and integrating the obtained knowledge into the code base. All of this is often done manually. We present Prompter, a plug-in for the Eclipse IDE which automatically searches and identifies relevant Stack Overflow discussions, evaluates their relevance given the code context in the IDE, and notifies the developer if and only if a user-defined confidence threshold is surpassed.

StORMeD: stack overflow ready made data

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.

CodeTube: extracting relevant fragments from software development video tutorials

Nowadays developers heavily rely on sources of informal documentation, including 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.DEMO URL: http://codetube.inf.usi.ch VIDEO URL: https://youtu.be/yUsUG3g87Dg

Summarizing complex development artifacts by mining heterogeneous data

Summarization is hailed as a promising approach to reduce the amount of information that must be taken in by the person who wants to understand development artifacts, such as pieces of code, bug reports, emails, etc. However, existing approaches treat artifacts as pure textual entities, disregarding the heterogeneous and partially structured nature of most artifacts, which contain intertwined pieces of distinct type, such as source code, diffs, stack traces, human language, etc. We present a novel approach to augment existing summarization techniques (such as LexRank) to deal with the heterogeneous and multidimensional nature of complex artifacts. Our preliminary results on heterogeneous artifacts suggest our approach outperforms the current text-based approaches.