research-article

ActionNet: vision-based workflow action recognition from programming screencasts

Authors:

Zhenchang Xing,

Guoqiang LiAuthors Info & Claims

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

Pages 350 - 361

https://doi.org/10.1109/ICSE.2019.00049

Published: 25 May 2019 Publication History

Abstract

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 programming screencasts. Using programming screencasts from Youtube, we evaluate different configurations of our CNN model and the performance of our technique for developer action recognition across developers, working environments and programming languages. Using screencasts of developers' real work, we demonstrate the usefulness of our technique in a practical application for action-aware extraction of key-code frames in developers' work.

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cover image ACM Conferences

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

May 2019

1318 pages

General Chair:
Joanne M. Atlee
University of Waterloo, Canada
,
Program Chairs:
Tevfik Bultan
University of California, Santa Barbara
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Jon Whittle
Monash University, Australia

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SIGSOFT: ACM Special Interest Group on Software Engineering
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IEEE Press

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Published: 25 May 2019

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ICSE '19: 41st International Conference on Software Engineering

May 25 - 31, 2019

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https://dl.acm.org/doi/10.1145/3611643.3613867
Wei ZWang HAshraf IChan W(2023)DeepPatch: Maintaining Deep Learning Model Programs to Retain Standard Accuracy with Substantial Robustness ImprovementACM Transactions on Software Engineering and Methodology10.1145/360460932:6(1-49)Online publication date: 14-Jun-2023
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