TY - GEN
T1 - Reverse engineering time-series interaction data from screen-captured videos
AU - Bao, Lingfeng
AU - Li, Jing
AU - Xing, Zhenchang
AU - Wang, Xinyu
AU - Zhou, Bo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/4/8
Y1 - 2015/4/8
N2 - In recent years the amount of research on human aspects of software engineering has increased. Many studies use screen-capture software (e.g., Snagit) to record developers' behavior as they work on software development tasks. The recorded task videos capture direct information about which activities the developers carry out with which content and in which applications during the task. Such behavioral data can help researchers and practitioners understand and improve software engineering practices from human perspective. However, extracting time-series interaction data (software usage and application content) from screen-captured videos requires manual transcribing and coding of videos, which is tedious and error-prone. In this paper we present a computer-vision based video scraping technique to automatically reverse-engineer time-series interaction data from screen-captured videos. We report the usefulness, effectiveness and runtime performance of our video scraping technique using a case study of the 29 hours task videos of 20 developers in the two development tasks.
AB - In recent years the amount of research on human aspects of software engineering has increased. Many studies use screen-capture software (e.g., Snagit) to record developers' behavior as they work on software development tasks. The recorded task videos capture direct information about which activities the developers carry out with which content and in which applications during the task. Such behavioral data can help researchers and practitioners understand and improve software engineering practices from human perspective. However, extracting time-series interaction data (software usage and application content) from screen-captured videos requires manual transcribing and coding of videos, which is tedious and error-prone. In this paper we present a computer-vision based video scraping technique to automatically reverse-engineer time-series interaction data from screen-captured videos. We report the usefulness, effectiveness and runtime performance of our video scraping technique using a case study of the 29 hours task videos of 20 developers in the two development tasks.
UR - http://www.scopus.com/inward/record.url?scp=84928675793&partnerID=8YFLogxK
U2 - 10.1109/SANER.2015.7081850
DO - 10.1109/SANER.2015.7081850
M3 - Conference contribution
T3 - 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2015 - Proceedings
SP - 399
EP - 408
BT - 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2015
Y2 - 2 March 2015 through 6 March 2015
ER -