Stack Overflow is a popular community-based Q&A website that caters to technical needs of software developers. As of February 2015 -- Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study, we propose a two stage hybrid approach -- DelPredictor -- which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features. We evaluate DelPredictor on 5 years (2008 -- 2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.

Stack Exchange (SE) is a network of popular Community based Question and Answering (CQA) websites. Each SE Q&A website is created to address questions on specific user interest or domain. However, often users post questions on SE websites that do not match the domain of the website. Such questions are considered as Off-topic for the origin site. Off-topic questions must be detected and migrated to more appropriate On-Topic site in SE network. Off-topic questions are migrated manually to other sites by moderators or experts users (through a voting process). The process of migrating questions from one site to other is known as Question Migration. We study migrated questions on SE Q&A website. We identify several distinguishing features of migrated questions and propose a machine learning based framework to predict migrating questions. Effectiveness of proposed model is tested on five SE Q&A sites. Experimental results demonstrate that the proposed model is effective (maximum accuracy of 73%) in predicting migrating questions.

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.

Several widely used Issue tracking systems (such as Google Issue Tracker and Bugzilla) contains an integrated threaded discussion forum to facilitate discussion between the development and maintenance team (bug reporters, bug triagers, bug fixers and quality assurance managers). We observe that several comments (and even bug report descriptions) posted to issue tracing system contains links to external websites as references to knowledge sources relevant to the discussion. We conduct a survey (and present the results of the survey) of Google Chromium Developers on the importance and usefulness of web references in issue tracking system comments and the need of a web-browser extension which facilitates easy organization and inclusion of web-links in the post. We conduct a characterization study on an experimental dataset from Google Chromium Issue Tracking system and present results on the distribution of number of links in the dataset, categorization of links into pre-defined classes (such as blogs, community based Q&A websites, developer discussion forums, version control system), correlation of number and types of links with various bug report types (such as security, crash, regression and clean-up) and relation between presence of links and bug resolution time. Survey results and data characterization study motivate the need of building a developer productivity tool to facilitate web-link (as references) organization and inclusion in issue tracking system comments. We present a Google Chromium Web Browser Extension called as Samekana and publish the extension on Google Chromium Web Store which can be freely downloaded by users worldwide. The extension contains features such as annotating (using tags, title and description) and saving web references pertaining to multiple bug reports and tasks and then posting it as bibliography (for easy citation and reference) in issue tracking system comments.

Twitter is a popular micro-blogging website which allows users to post 140-character limit messages called tweets. We demonstrate a cheap and elegant solution - WhACKY! - to harness the multi-source information from tweets to link Twitter profiles across other external services. In particular, we exploit activity feed sharing patterns to map Twitter profiles to their corresponding external service accounts using publicly available APIs. We illustrate a proof-of-concept by mapping 69,496 Twitter profiles to at least one of the five popular external services : Flickr (photo-sharing service), Foursquare (location-based service), YouTube (video-sharing service), Facebook (a popular social network) and LastFM (music-sharing service). We evaluate our solution against a commercial social identity mapping service - FlipTop - and demonstrate the efficiency of our approach. WhACKY! guarantees that the mapped profiles are 100% true-positive and helps quantify the unintended leakage of Personally Identifiable Information (PII) attributes. During the process, WhACKY! is also able to detect duplicate Twitter profiles connected to multiple external services.We also develop a web application based on WhACKY!1 for perusal by Twitterers which can help them better understand unintended leakage of their PII.