Efficient Search of Live-Coding Screencasts from Online Videos

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ID: 283544
2022
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Abstract
Programming videos on the Internet are valuable resources for learning programming skills. To find relevant videos, developers typically search online video platforms (e.g., YouTube) with keywords on topics they wish to learn. Developers often look for live-coding screencasts, in which the videos' authors perform live coding. Yet, not all programming videos are live-coding screencasts. In this work, we develop a tool named PSFinder to identify live-coding screencasts. PSFinder leverages a classifier to identify whether a video frame contains an IDE window. It uses a sampling strategy to pick a number of frames from an input video, runs the classifer on these frames, and then determines whether the video is a live-coding screencast based on frames classified as containing IDE window. In our preliminary experiment, PSFinder can effectively identify live-coding screencasts as it achieves an F1-score of 0.97.
Reference Key
lo2022efficient Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chengran Yang; Ferdian Thung; David Lo
Journal arXiv
Year 2022
DOI
DOI not found
URL
http://arxiv.org/abs/2203.04519v1
Keywords
cs.se cs.mm

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