Toward Mining Visual Log of Software

TL;DR

This paper introduces a framework for mining visual logs of software, such as screenshots and recordings, to aid debugging, testing, UI design, and support by detecting GUI elements, user interactions, and patterns.

Contribution

It proposes a comprehensive framework for extracting and analyzing visual logs, including techniques for GUI element detection, interaction recognition, and pattern learning, supported by a study on mobile app GUIs.

Findings

Heuristics for recognizing GUI elements in mobile apps

Insights into GUI element characteristics in mobile applications

Potential applications in bug reproduction and automated testing

Abstract

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.