Toward the Automated Localization of Buggy Mobile App UIs from Bug Descriptions
This program is tentative and subject to change.
Bug report management is a costly software maintenance process comprised of several challenging tasks. Given the UI-driven nature of mobile apps, bugs typically manifest through the UI, hence the identification of buggy UI screens and UI components (Buggy UI Localization) is important to localizing the buggy behavior and eventually fixing it. However, this task is challenging as developers must reason about bug descriptions (which are often low-quality), and the visual or code representations of UI screens.
This paper is the first to investigate the feasibility of automating the task of Buggy UI Localization through a comprehensive study that evaluates the capabilities of one textual and two multi-modal deep learning (DL) techniques and one textual unsupervised technique. We evaluate such techniques at two levels of granularity, Buggy UI Screen and UI Component localization. Our results illustrate the individual strengths of models that make use of different representations, wherein models that incorporate visual information perform better for UI screen localization, and models that operate on textual screen information perform better for UI component localization – highlighting the need for a localization approach that blends the benefits of both types of techniques. Furthermore, we study whether Buggy UI Localization can improve traditional buggy code localization and find that incorporating localized buggy UIs leads to improvements of 9%-12% in Hits@10.
This program is tentative and subject to change.
Wed 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:30 - 11:50 | |||
10:30 20mTalk | Toward the Automated Localization of Buggy Mobile App UIs from Bug Descriptions Technical Papers Antu Saha William & Mary, Yang Song College of William and Mary, Junayed Mahmud University of Central Florida, Ying Zhou George Mason University, USA, Kevin Moran University of Central Florida, Oscar Chaparro William & Mary | ||
10:50 20mTalk | Reproducing Timing-dependent GUI Flaky Tests in Android Apps via A Single Event Delay Technical Papers Xiaobao Cai Fudan University, Zhen Dong Fudan University, China, Yongjiang Wang Fudan University, Abhishek Tiwari Software Institute - USI, Lugano, Switzerland, Xin Peng Fudan University | ||
11:10 20mTalk | Semantic Constraint Inference for Web Form Test Generation Technical Papers Parsa Alian University of British Columbia, Noor Nashid University of British Columbia, Mobina Shahbandeh University of British Columbia, Ali Mesbah The University of British Columbia | ||
11:30 20mTalk | Guardian: A Runtime Framework for LLM-based UI Exploration Technical Papers Dezhi Ran Peking University, Hao Wang Peking University, China, Zihe Song University of Texas at Dallas, Mengzhou Wu Peking University, Yuan Cao Peking University, Ying Zhang Peking University, Wei Yang University of Texas at Dallas, Tao Xie Peking University |