Open Collections

Automatic conceptual window grouping with frequent pattern matching

University of British Columbia

While working, software developers constantly switch between different projects and tasks and use many different applications, web resources and files. These diverse resources are scattered across many windows and lead to cluttered workspaces that can distract developers in their workflows. Having mechanisms to determine which resources belong together for working on a project, would allow us to develop tools that could support developers in organizing their work, declutter their workspace and switch between projects. Existing approaches in this area often either require users to manually define which resources belong together, or do not examine how users would group the resources themselves and how to best support them. In this thesis we present an approach that automatically detects groups of applications and resources that developers use and are relevant to the tasks and projects they are working on. These groups are referred to as Conceptual Groups. The approach applies frequent pattern analysis on recorded interaction data and clusters these to retrieve conceptual groups. To measure the accuracy of our approach, we conducted a study with 11 participants and compared it to existing approaches which were outperformed by up to 50%.

Text

2019-10-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71996

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/