Automatic classification of software related microblogs

Abstract—Millions of people, including those in the soft-ware engineering communities have turned to microblogging services, such as Twitter, as a means to quickly disseminate information. A number of past studies by Treude et al., Storey, and Yuan et al. have shown that a wealth of interesting information is stored in these microblogs. However, microblogs also contain a large amount of noisy content that are less relevant to software developers in engineering software systems. In this work, we perform a preliminary study to investigate the feasibility of automatic classification of microblogs into two categories: relevant and irrelevant to engineering software systems. We extract features from the textual content of the microblogs and the titles of any URLs mentioned in the mi-croblogs. These features are then used to learn a discriminative model used in classifying relevant and irrelevant microblogs. We show that our trained model can achieve a promising classification performance. I

What does software engineering community microblog about?

Abstract—Microblogging is a new trend to communicate and to disseminate information. One microblog post could potentially reach millions of users. Millions of microblogs are generated on a daily basis on popular sites such as Twitter. The popularity of microblogging among programmers, software engineers, and software users has also led to their use of microblogs to communicate software engineering issues apart from using emails and other traditional communication channels. Understanding how millions of users use microblogs in software engineering related activities would shed light on ways we could leverage the fast evolving microblogging content to aid software development efforts. In this work, we perform a preliminary study on what software engineering community microblogs about. We analyze the content of microblogs from Twitter and categorize the types of microblogs that are posted. We investigate the relative popularity of each category of mi-croblogs. We also investigate what kinds of microblogs that are diffused more widely in the Twitter network. Our experiments show that microblogs commonly contain job openings, news, questions and answers, or links to download new tools and code. We find that microblogs concerning the real-world events are more widely diffused in the Twitter network. I

Tag Recommendation in Software Information Sites

Abstract—Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers im-prove their performance in software development, maintenance and test processes as software information sites. It is common to see tags in software information sites and many sites allow users to tag various objects with their own words. Users increasingly use tags to describe the most important features of their posted contents or projects. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software in-formation sites. TagCombine has 3 different components: 1. multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2. similarity based rankin