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ESEC/FSE 2022
Mon 14 - Fri 18 November 2022 Singapore
Mon 14 Nov 2022 11:30 - 11:45 at SRC LT 50 - Software Testing I

Symbolic execution has become a foundational program analysis technique. Performing symbolic execution unavoidably encounters internal functions (e.g. library functions) that provide basic operations such as string processing. Many symbolic execution engines construct internal function models that abstract function behaviors for scalability and compatibility concerns. Due to the high complexity of constructing the models, developers intentionally summarize only partial behaviors of a function, namely modeled functionalities, in the models. The correctness of the internal function models is critical because it would impact all applications of symbolic execution, e.g. bug detection and model checking. %, vulnerability assessment, and model checking.

A naive solution to testing the correctness of internal function models is to cross-check whether the behaviors of the models comply with their corresponding original function implementations. However, such a solution would mostly detect overwhelming inconsistencies concerning the unmodeled functionalities, which are out of the scope of models and thus considered false reports. We argue that a reasonable testing approach should target only the functionalities that developers intend to model. While being necessary, automatically identifying the modeled functionalities, i.e. the scope, is a significant challenge.

In this paper, we propose a scope-aware differential testing framework, SEDiff, to tackle this problem. SEDiff designs a novel algorithm to automatically map the modeled functionalities to the code in the original implementations. It then applies scope-aware grey-box differential fuzzing to relevant code in the original implementations. SEDiff also equips a new scope-guided input generator and a tailored bug checker that efficiently and correctly detect erroneous inconsistencies. %concerning only the modeled functionalities. We extensively evaluated SEDiff on several popular real-world symbolic execution engines targeting binary, web and kernel. Our manual investigation shows that SEDiff precisely identifies the modeled functionalities and detects 46 new bugs in the internal function models used in the symbolic execution engines.

Mon 14 Nov

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

11:00 - 12:30
Software Testing IResearch Papers at SRC LT 50
11:00
15m
Talk
Testing of Autonomous Driving Systems: Where are we and where should we go?
Research Papers
Guannan Lou Macquarie University, Yao Deng Macquarie University, Xi Zheng Macquarie University, Mengshi Zhang Meta, Tianyi Zhang Purdue University
11:15
15m
Talk
Fuzzing Deep-Learning Libraries via Automated Relational API Inference
Research Papers
Yinlin Deng University of Illinois at Urbana-Champaign, Chenyuan Yang University of Illinois at Urbana-Champaign, Anjiang Wei Stanford University, Lingming Zhang University of Illinois at Urbana-Champaign
11:30
15m
Talk
SEDiff: Scope-Aware Differential Fuzzing to Test Internal Function Models in Symbolic Execution
Research Papers
Penghui Li The Chinese University of Hong Kong, Wei Meng Chinese University of Hong Kong, Kangjie Lu University of Minnesota
11:45
15m
Talk
Perfect Is the Enemy of Test Oracle
Research Papers
Ali Reza Ibrahimzada University of Illinois, Urbana-Champaign, Yigit Varli Middle East Technical University, Dilara Tekinoglu University of Massachusetts, Amherst, Reyhaneh Jabbarvand University of Illinois, Urbana-Champain
12:00
15m
Talk
Scenario-based Test Reduction and Prioritization for Multi-Module Autonomous Driving Systems
Research Papers
Yao Deng Macquarie University, Xi Zheng Macquarie University, Mengshi Zhang Meta, Guannan Lou Macquarie University, Tianyi Zhang Purdue University
12:15
15m
Talk
MOSAT: Finding Safety Violations of Autonomous Driving Systems Using Multi-Objective Genetic Algorithm
Research Papers
Haoxiang Tian Institute of Software, Chinese Academy of Sciences, Yan Jiang Institute of Software, Chinese Academy of Sciences, Guoquan Wu Institute of Software at Chinese Academy of Sciences, China, Jiren Yan Institute of Software, Chinese Academy of Sciences, Jun Wei Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Wei Chen Institute of Software at Chinese Academy of Sciences, China, Shuo Li Institute of Software, Chinese Academy of Sciences, Dan Ye Institute of Software at Chinese Academy of Sciences
DOI