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Programming screencasts have two important applications in software engineering context: study developer behaviors, information needs and disseminate software engineering knowledge. Although programming screencasts are easy to produce, they are not easy to analyze or index due to the image nature of the data. Existing techniques extract only content from screencasts, but ignore workflow actions by which developers accomplish programming tasks. This significantly limits the effective use of programming screencasts in downstream applications. In this paper, we are the first to present a novel technique for recognizing workflow actions in programming screencasts. Our technique exploits image differencing and Convolutional Neural Network (CNN) to analyze the correspondence and change of consecutive frames, based on which nine classes of frequent developer actions can be recognized from …