Efficient Search of Live-Coding Screencasts from Online Videos

TL;DR

This paper introduces PSFinder, a tool that accurately identifies live-coding screencasts in online programming videos by classifying video frames to find IDE windows, achieving high precision and recall.

Contribution

The paper presents a novel frame-based classification approach and sampling strategy for detecting live-coding screencasts in online videos, improving search relevance.

Findings

Achieved an F1-score of 0.97 in identifying live-coding screencasts.

Demonstrated effective classification of IDE windows in video frames.

Proposed a sampling method to efficiently analyze videos for live coding content.

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.