Toward Mining Visual Log of Software
This work addresses software debugging, testing, and design challenges for developers and users, but it is incremental as it builds on existing GUI analysis concepts.
The paper tackles the problem of mining visual logs of software, which capture user-GUI interactions, by proposing a core framework for detecting GUI elements and changes, removing private data, recognizing interactions, and learning usage patterns, with a small study on mobile app GUI characteristics providing heuristics for technique design.
In this paper, we define visual log of a software system as data capturing the interactions between its users and its graphic user interface (GUI), such as screen-shots and screen recordings. We vision that mining such visual log could be useful for bug reproducing and debugging, automated GUI testing, user interface designing, question answering of common usages in software support, etc. Toward that vision, we propose a core framework for mining visual log of software. This framework focuses on detecting GUI elements and changes in visual log, removing users' private data, recognizing user interactions with GUI elements, and learning GUI usage patterns. We also performed a small study on the characteristics of GUI elements in mobile apps. The findings from this study suggested several heuristics to design techniques for recognizing GUI elements and interactions.