@inproceedings{8ae27de8369949b692317afcd3e876af,
title = "BPMiner: Mining developers' behavior patterns from screen-captured task videos",
abstract = "Many user studies of software development use screen-capture software to record developers' behavior for post-mortem analysis. However, extracting behavioral patterns from screencaptured videos requires manual transcription and coding of videos, which is often tedious and error-prone. Automatically extracting Human-Computer Interaction (HCI) data from screen-captured videos and systematically analyzing behavioral data will help researchers analyze developers' behavior in software development more effectively and efficiently. In this paper, we present BPMiner, a novel behavior analysis approach to mine developers' behavior patterns from screencaptured videos using computer vision techniques and exploratory sequential pattern analysis. We have implemented a proof-of-concept prototype of BPMiner, and applied the BPMiner prototype to study the developers' online search behavior during software development. Our study suggests that the BPMiner approach can open up new ways to study developers' behavior in software development.",
keywords = "Developers' behavior, HCI data, Online search, Screen-captured video, Software development",
author = "Jing Li and Lingfeng Bao and Zhenchang Xing and Xinyu Wang and Bo Zhou",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 31st Annual ACM Symposium on Applied Computing, SAC 2016 ; Conference date: 04-04-2016 Through 08-04-2016",
year = "2016",
month = apr,
day = "4",
doi = "10.1145/2851613.2851771",
language = "English",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery (ACM)",
pages = "1371--1377",
booktitle = "2016 Symposium on Applied Computing, SAC 2016",
address = "United States",
}