R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing
Tue 10 May 2022 22:15 - 22:20 at ICSE room 5-even hours - Software Testing 6 Chair(s): Leonardo Sousa
A rendering regression is a bug introduced by a web browser where a web page no longer functions as users expect. Such rendering bugs critically harm the usability of web browsers as well as web applications. The unique aspect of rendering bugs is that they affect the presented visual appearance of web pages, but those web pages have no pre-defined correct appearance. Therefore, it is challenging to automatically detect errors in their appearance. In practice, web browser vendors rely on non-trivial and time-prohibitive manual analysis to detect and handle rendering regressions.
This paper proposes R2Z2, an automated tool to find rendering regressions. R2Z2 uses the differential fuzz testing approach, which repeatedly compares the rendering results of two different versions of a browser while providing the same HTML as input. If the rendering results are different, R2Z2 further performs cross browser compatibility testing to check if the rendering difference is indeed a rendering regression. After identifying a rendering regression, R2Z2 will perform an in-depth analysis to aid in fixing the regression. Specifically, R2Z2 performs a delta-debugging-like analysis to pinpoint the exact browser source code commit causing the regression, as well as inspecting the rendering pipeline stages to pinpoint which pipeline stage is responsible. We implemented a prototype of R2Z2 particularly targeting the Chrome browser. So far, R2Z2 found 11 previously undiscovered rendering regressions in Chrome, all of which were confirmed by the Chrome developers. Importantly, in each case, R2Z2 correctly reported the culprit commit. Moreover, R2Z2 correctly pin-pointed the culprit rendering pipeline stage in all but one case.
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 |