Automatic Tag Recommendation for Software Development Video Tutorials
Software development video tutorials are emerging as a new resource for developers to support their information needs. However, when trying to find the right video to watch for a task at hand, developers have little information at their disposal to quickly decide if they found the right video or not. This can lead to missing the best tutorials or wasting time watching irrelevant ones.
Other external sources of information for developers, such as StackOverflow, have benefited from the existence of informative tags, which help developers to quickly gauge the relevance of posts and find related ones. We argue that the same is valid also for videos and propose the first set of approaches to automatically generate tags describing the contents of software development video tutorials. We investigate seven tagging approaches for this purpose, some using information retrieval techniques and leveraging only the information in the videos, others relying on external sources of information, such as StackOverflow, as well as two out-of-the-box commercial video tagging approaches. We evaluated 19 different configurations of these tagging approaches and the results of a user study showed that some of the information retrieval-based approaches performed the best and were able to recommend tags that developers consider relevant for describing programming videos.