BPMiner: Mining developers' behavior patterns from screen-captured task videos

Jing Li, Lingfeng Bao, Zhenchang Xing, Xinyu Wang, Bo Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 Symposium on Applied Computing, SAC 2016
PublisherAssociation for Computing Machinery
Pages1371-1377
Number of pages7
ISBN (Electronic)9781450337397
DOIs
Publication statusPublished - 4 Apr 2016
Externally publishedYes
Event31st Annual ACM Symposium on Applied Computing, SAC 2016 - Pisa, Italy
Duration: 4 Apr 20168 Apr 2016

Publication series

NameProceedings of the ACM Symposium on Applied Computing
Volume04-08-April-2016

Conference

Conference31st Annual ACM Symposium on Applied Computing, SAC 2016
Country/TerritoryItaly
CityPisa
Period4/04/168/04/16

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