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Computer Science > Machine Learning

arXiv:2409.18556 (cs)
[Submitted on 27 Sep 2024]

Title:CodeSCAN: ScreenCast ANalysis for Video Programming Tutorials

Authors:Alexander Naumann, Felix Hertlein, Jacqueline Höllig, Lucas Cazzonelli, Steffen Thoma
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Abstract:Programming tutorials in the form of coding screencasts play a crucial role in programming education, serving both novices and experienced developers. However, the video format of these tutorials presents a challenge due to the difficulty of searching for and within videos. Addressing the absence of large-scale and diverse datasets for screencast analysis, we introduce the CodeSCAN dataset. It comprises 12,000 screenshots captured from the Visual Studio Code environment during development, featuring 24 programming languages, 25 fonts, and over 90 distinct themes, in addition to diverse layout changes and realistic user interactions. Moreover, we conduct detailed quantitative and qualitative evaluations to benchmark the performance of Integrated Development Environment (IDE) element detection, color-to-black-and-white conversion, and Optical Character Recognition (OCR). We hope that our contributions facilitate more research in coding screencast analysis, and we make the source code for creating the dataset and the benchmark publicly available on this website.
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2409.18556 [cs.LG]
  (or arXiv:2409.18556v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2409.18556

Submission history

From: Alexander Naumann [view email]
[v1] Fri, 27 Sep 2024 08:53:17 UTC (2,292 KB)
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