NDLI: Extracting code from programming tutorial videos

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Extracting code from programming tutorial videos

Content Provider	ACM Digital Library
Author	Yahav, Eran Yadid, Shir
Abstract	The number of programming tutorial videos on the web increases daily. Video hosting sites such as YouTube host millions of video lectures, with many programming tutorials for various languages and platforms. These videos contain a wealth of valuable information, including code that may be of interest. However, two main challenges have so far prevented the effective indexing of programming tutorial videos: (i) code in tutorials is typically written on-the-fly, with only parts of the code visible in each frame, and (ii) optical character recognition (OCR) is not precise enough to produce quality results from videos. We present a novel approach for extracting code from videos that is based on: (i) consolidating code across frames, and (ii) statistical language models for applying corrections at different levels, allowing us to make corrections by choosing the most likely token, combination of tokens that form a likely line structure, and combination of lines that lead to a likely code fragment in a particular language. We implemented our approach in a tool called ACE, and used it to extract code from 40 Android video tutorials on YouTube. Our evaluation shows that ACE extracts code with high accuracy, enabling deep indexing of video tutorials. .
Starting Page	98
Ending Page	111
Page Count	14
File Format	PDF
ISBN	9781450340762
DOI	10.1145/2986012.2986021
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical language models Video tutorials Programmer education
Content Type	Text
Resource Type	Article

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