GIFdroid: Automated Replay of Visual Bug Reports for Android Apps
Tue 10 May 2022 22:25 - 22:30 at ICSE room 5-even hours - Software Testing 6 Chair(s): Leonardo Sousa
Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using software. However, it is difficult for non-technical users to write clear descriptions about the bug occurrence. Therefore, more and more users begin to record the screen for reporting bugs as it is easy to be created and contains detailed procedures triggering the bug. But it is still a tedious and time-consuming for developers to reproduce the bug due to the length and unclear actions within the recording. To overcome these issues, we propose GIFdroid, a light-weight approach to automatically replay the execution trace from visual bug reports. GIFdroid adopts image processing techniques to extract the keyframes from the recording, map them to states in GUI Transitions Graph, and generate the execution trace of those states to trigger the bug. Our automated experiments and user study demonstrate its accuracy, efficiency, and usefulness of the approach.
Tue 10 MayDisplayed time zone: Eastern Time (US & Canada) change
22:00 - 23:00 | Software Testing 6SEIP - Software Engineering in Practice / Technical Track / Journal-First Papers at ICSE room 5-even hours Chair(s): Leonardo Sousa | ||
22:00 5mTalk | Algorithmic Profiling for Real-World Complexity Problems Journal-First Papers Boqin Qin China Telecom Cloud Computing Corporation, Tengfei Tu Beijing University of Posts and Telecommunications, Ziheng Liu University of California, San Diego, Tingting Yu University of Cincinnati, Linhai Song Pennsylvania State University, USA DOI Pre-print Media Attached | ||
22:05 5mTalk | To What Extent Do DNN-based Image Classification Models Make Unreliable Inferences? Journal-First Papers Yongqiang TIAN The Hong Kong University of Science and Technology; University of Waterloo, Shiqing Ma Rutgers University, Ming Wen Huazhong University of Science and Technology, Yepang Liu Southern University of Science and Technology, Shing-Chi Cheung Hong Kong University of Science and Technology, Xiangyu Zhang Purdue University DOI Pre-print Media Attached | ||
22:10 5mTalk | Testing Machine Learning Systems in Industry: An Empirical Study SEIP - Software Engineering in Practice Shuyue Li Xi'an Jiaotong University, Jiaqi Guo Xi'an Jiaotong University, Jian-Guang Lou Microsoft Research, Ming Fan Xi'an Jiaotong University, Ting Liu Xi'an Jiaotong University, Dongmei Zhang Microsoft Research DOI Pre-print Media Attached | ||
22:15 5mTalk | R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing Technical Track Suhwan Song Seoul National University, South Korea, Jaewon Hur Seoul National University, Sunwoo Kim Samsung Research, Samsung Electronics, Philip Rogers Google, Byoungyoung Lee Seoul National University, South Korea Pre-print Media Attached | ||
22:20 5mTalk | Fuzzing Class Specifications Technical Track Facundo Molina University of Rio Cuarto and CONICET, Argentina, Marcelo d'Amorim Federal University of Pernambuco, Nazareno Aguirre University of Rio Cuarto and CONICET, Argentina Pre-print Media Attached | ||
22:25 5mTalk | GIFdroid: Automated Replay of Visual Bug Reports for Android Apps Technical Track DOI Pre-print Media Attached | ||