Combining biometric data with focused document types classifies a success of program comprehension
Program comprehension is one of the important cognitive processes in software maintenance. The process typically involves diverse mental activities such as understanding of source code, library usages, and requirements. Systematic supports would be improved if the supports can be aware of such fine-grained mental activities during program comprehension. Here we aim to investigate whether biometric data can be varied according to such mental activity classes and conduct an experiment with program comprehension tasks involving multiple documents. As a result, we successfully classified the success/failure of the tasks at 85.2% from electroencephalogram (EEG) combined with focused document types. This result suggests that our metrics based on EEG and focused document types might be beneficial to detect developers’ diverse mental activities triggered by different documents.
Wed 15 Jul Times are displayed in time zone: (UTC) Coordinated Universal Time change
00:00 - 01:00 | Session 9: For DevelopersResearch / Tool Demonstration / ERA at ICPC Chair(s): Anderson OliveiraPUC-Rio | ||
00:00 12mPaper | An Empirical Study on Dynamic Typing Related Practices in Python Systems Research Zhifei ChenNanjing University, Yanhui LiDepartment of Computer Science and Technology, Nanjing University, Bihuan ChenFudan University, Wanwangying MaNanjing University, Lin ChenNanjing University, Baowen XuNanjing University Media Attached | ||
00:12 12mPaper | Performing Tasks Can Improve Program Comprehension Mental Model of Novice Research Amal A. ShargabiQassim University, Syed Ahmad AljunidUniversiti Teknologi MARA, Muthukkaruppan AnnamalaiUniversiti Teknologi MARA, Abdullah Mohd ZinUniversiti Kebangsaan Malaysia Media Attached | ||
00:24 12mPaper | SimplyHover: Improving Comprehension of else Statements Tool Demonstration Ahmad JbaraComputer and Cyber Sciences, Augusta University, Georgia, USA, Bar Ben Michael, Or Shacham, Omer Tavor Media Attached | ||
00:36 12mPaper | Combining biometric data with focused document types classifies a success of program comprehension ERA Toyomi IshidaNara Institute of Science and Technology, Hidetake UwanoNational Institute of Technology, Nara College, Japan, Yoshiharu Ikutani Nara Institute of Science and Technology Media Attached | ||
00:48 12mPaper | Program Comprehension in Virtual Reality ERA James DominicClemson University, Brock TubreClemson Universtiy, Jada Houser Clemson University, Charles RitterClemson University, Deborah KunkelClemson University, Paige RodegheroClemson University Media Attached | ||