ISSTA 2021
30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2021)
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30th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2021), July 11–17, 2021, Virtual, Denmark

ISSTA 2021 – Preliminary Table of Contents

Contents - Abstracts - Authors


Title Page

Message from the Chairs



Accepted Papers

Seed Selection for Successful Fuzzing
Adrian Herrera, Hendra Gunadi, Shane Magrath, Michael Norrish, Mathias Payer, and Antony L. Hosking
(Australian National University, Australia; DSTG, Australia; Data61 at CSIRO, Australia; EPFL, Switzerland)

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Finding Data Compatibility Bugs with JSON Subschema Checking
Andrew Habib, Avraham Shinnar, Martin Hirzel, and Michael Pradel
(TU Darmstadt, Germany; IBM Research, USA; University of Stuttgart, Germany)

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Fixing Dependency Errors for Python Build Reproducibility
Suchita Mukherjee, Abigail Almanza, and Cindy Rubio-González
(University of California at Davis, USA)

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Model-Based Testing of Networked Applications
Yishuai LiORCID logo, Benjamin C. Pierce, and Steve Zdancewic
(University of Pennsylvania, USA)

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Challenges and Opportunities: An In-Depth Empirical Study on Configuration Error Injection Testing
Wang Li, Zhouyang Jia, Shanshan Li, Yuanliang Zhang, Teng Wang, Erci Xu, Ji Wang, and Liao Xiangke
(National University of Defense Technology, China)

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WebEvo: TamingWeb Application Evolution via Detecting Semantic Structure Change
Fei Shao, Rui Xu, Wasif Haque, Jingwei Xu, Ying Zhang, Wei Yang, Yanfang Ye, and Xusheng Xiao
(Case Western Reserve University, USA; University of Texas at Dallas, USA; Peking University, China)

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Exposing Previously Undetectable Faults in Deep Neural Networks
Isaac Dunn, Hadrien Pouget, Daniel Kroening, and Tom Melham
(University of Oxford, UK; Amazon, n.n.)

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Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)
Fitash Ul Haq, Donghwan ShinORCID logo, Lionel C. Briand, Thomas Stifter, and Jun Wang
(University of Luxembourg, Luxembourg; University of Ottawa, Canada; IEE, n.n.; Post Luxembourg, Luxembourg)

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Fuzzing SMT Solvers via Two-Dimensional Input Space Exploration
Peisen Yao ORCID logo, Heqing Huang, Wensheng Tang, Qingkai Shi, Rongxin Wu, and Charles Zhang
(Hong Kong University of Science and Technology, China; Xiamen University, China)

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A Lightweight Framework for Function Name Reassignment Based on Large-Scale Stripped Binaries
Han Gao, Shaoyin Cheng, Yinxing Xue, and Weiming Zhang
(University of Science and Technology of China, China)

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Faster, Deeper, Easier: Crowdsourcing Diagnosis of Microservice Kernel Failure from User Space
Yicheng Pan, Meng Ma, Xinrui Jiang, and Ping Wang
(Peking University, China)
With the widespread use of cloud-native architecture, increasing web applications (apps) choose to build on microservices. Simultaneously, troubleshooting becomes full of challenges owing to the high dynamics and complexity of anomaly propagation. Existing diagnostic methods rely heavily on monitoring metrics collected from the kernel side of microservice systems. Without a comprehensive monitoring infrastructure, application owners and even cloud operators cannot resort to these kernel-space solutions. This paper summarizes several insights on operating a top commercial cloud platform. Then, for the first time, we put forward the idea of user-space diagnosis for microservice kernel failures. To this end, we develop a crowdsourcing solution - DyCause, to resolve the asymmetric diagnostic information problem. DyCause deploys on the application side in a distributed manner. Through lightweight API log sharing, apps collect the operational status of kernel services collaboratively and initiate diagnosis on demand. Deploying DyCause is fast and lightweight as we do not have any architectural and functional requirements for the kernel. To reveal more accurate correlations from asymmetric diagnostic information, we design a novel statistical algorithm that can efficiently discover the time-varying causalities between services. This algorithm also helps us build the temporal order of the anomaly propagation. Therefore, by using DyCause, we can obtain more in-depth and interpretable diagnostic clues with limited indicators. We apply and evaluate DyCause on both a simulated test-bed and a real-world cloud system. Experimental results verify that DyCause running in the user-space outperforms several state-of-the-art algorithms running in the kernel on accuracy. Besides, DyCause shows superior advantages in terms of algorithmic efficiency and data sensitivity. Simply put, DyCause produces a significantly better result than other baselines when analyzing much fewer or sparser metrics. To conclude, DyCause is faster to act, deeper in analysis, and easier to deploy.

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Understanding and Finding System Setting-Related Defects in Android Apps
Jingling Sun, Ting Su, Junxin Li, Zhen Dong, Geguang Pu, Tao Xie, and Zhendong Su
(East China Normal University, China; National University of Singapore, Singapore; Peking University, China; ETH Zurich, Switzerland)

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Runtime Detection of Memory Errors with Smart Status
Zhe Chen, Chong Wang, Junqi Yan, Yulei Sui, and Jingling Xue
(Nanjing University of Aeronautics and Astronautics, China; University of Technology Sydney, Australia; UNSW, Australia)

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Interval Constraint-Based Mutation Testing of Numerical Specifications
Clothilde Jeangoudoux, Eva Darulova, and Christoph Lauter
(MPI-SWS, Germany; University of Alaska at Anchorage, USA; Sorbonne University, France)

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AdvDoor: Adversarial Backdoor Attack of Deep Learning System
Quan Zhang, Yifeng Ding, Yongqiang TianORCID logo, Jianmin Guo, Min Yuan, and Yu Jiang
(Tsinghua University, China; University of Waterloo, Canada; WeBank, n.n.)

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Test-Case Prioritization for Configuration Testing
Runxiang Cheng, Lingming Zhang, Darko Marinov, and Tianyin Xu
(University of Illinois at Urbana-Champaign, USA)

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DeepHyperion: Exploring the Feature Space of Deep Learning-Based Systems through Illumination Search
Tahereh Zohdinasab ORCID logo, Vincenzo Riccio ORCID logo, Alessio Gambi, and Paolo Tonella ORCID logo
(USI Lugano, Switzerland; University of Passau, Germany)

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Semantic Table Structure Identification in Spreadsheets
Yakun Zhang, Xiao Lv, Haoyu Dong, Wensheng Dou, Shi Han, Dongmei Zhang, Jun Wei, and Dan Ye
(Institute of Software at Chinese Academy of Sciences, China; Microsoft Research, n.n.; Microsoft Research, China)

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Boosting Symbolic Execution via Constraint Solving Time Prediction (Experience Paper)
Sicheng Luo, Hui Xu, Yanxiang Bi, Yangfan Zhou, and Xin Wang
(Fudan University, China)

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Gramatron: Effective Grammar-Aware Fuzzing
Prashast Srivastava and Mathias Payer
(Purdue University, USA; EPFL, Switzerland)

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Synthesize Solving Strategy for Symbolic Execution
Zhenbang Chen, Zehua Chen, Ziqi Shuai, Guofeng Zhang, Weiyu Pan, Yufeng Zhang, and Ji Wang
(National University of Defense Technology, China; Hunan University, China)

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ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Yuanchun Li, Ziqi Zhang, Bingyan Liu, Ziyue Yang, and Yunxin Liu
(Microsoft Research, China; Peking University, China)

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QFuzz: Quantitative Fuzzing for Side Channels
Yannic NollerORCID logo and Saeid Tizpaz-Niari
(National University of Singapore, Singapore; University of Texas at El Paso, USA)

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SAND: A Static Analysis Approach for Detecting SQL Antipatterns
Yingjun Lyu, Sasha Volokh, William G. J. Halfond, and Omer Tripp
(Amazon, USA; University of Southern California, USA)

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Deep Just-in-Time Defect Prediction: How Far Are We?
Zhengran Zeng, Yuqun Zhang, Haotian Zhang, and Lingming Zhang
(Southern University of Science and Technology, China; Ant Group, n.n.; University of Illinois at Urbana-Champaign, USA)

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Efficient White-Box Fairness Testing through Gradient Search
Lingfeng Zhang ORCID logo, Yueling Zhang ORCID logo, and Min Zhang ORCID logo
(East China Normal University, China; Singapore Management University, Singapore)
Deep learning (DL) systems are increasingly deployed for autonomous decision-making in a wide range of applications. Apart from the robustness and safety, fairness is also an important property that a well-designed DL system should have. To evaluate and improve individual fairness of a model, systematic test case generation for identifying individual discriminatory instances in the input space is essential. In this paper, we propose a framework EIDIG for efficiently discovering individual fairness violation. Our technique combines a global generation phase for rapidly generating a set of diverse discriminatory seeds with a local generation phase for generating as many individual discriminatory instances as possible around these seeds under the guidance of the gradient of the model output. In each phase, prior information at successive iterations is fully exploited to accelerate convergence of iterative optimization or reduce frequency of gradient calculation. Our experimental results show that, on average, our approach EIDIG generates 19.11% more individual discriminatory instances with a speedup of 121.49% when compared with the state-of-the-art method and mitigates individual discrimination by 80.03% with a limited accuracy loss after retraining.

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Automated Patch Backporting in Linux (Experience Paper)
Ridwan ShariffdeenORCID logo, Xiang Gao, Gregory J. Duck, Shin Hwei TanORCID logo, Julia Lawall, and Abhik RoychoudhuryORCID logo
(National University of Singapore, Singapore; Southern University of Science and Technology, China; Inria, France)

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Attack as Defense: Characterizing Adversarial Examples using Robustness
Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei YangORCID logo, Fu Song, and Jun Sun
(ShanghaiTech University, China; Zhejiang University, China; Singapore Management University, Singapore)

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Rethinking Android Taint Analysis Evaluations: A Study on the Impact of Tool Configuration Spaces
Austin Mordahl and Shiyi Wei
(University of Texas at Dallas, USA)

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Log-Based Slicing for System-Level Test Cases
Salma Messaoudi, Donghwan ShinORCID logo, Annibale Panichella, Domenico Bianculli, and Lionel C. Briand
(University of Luxembourg, Luxembourg; Delft University of Technology, Netherlands; University of Ottawa, Canada)

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DeepCrime: Mutation Testing of Deep Learning Systems Based on Real Faults
Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella ORCID logo
(USI Lugano, Switzerland)

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Type and Interval Aware Array Constraint Solving for Symbolic Execution
Ziqi Shuai, Zhenbang Chen, Yufeng Zhang, Jun Sun, and Ji Wang
(National University of Defense Technology, China; Hunan University, China; Singapore Management University, Singapore)

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Semantic Matching of GUI Events for Test Reuse: Are We There Yet?
Leonardo Mariani, Ali Mohebbi, Mauro Pezzè, and Valerio Terragni
(University of Milano-Bicocca, Italy; USI Lugano, Switzerland; Schaffhausen Institute of Technology, Switzerland; University of Auckland, New Zealand)

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An Infrastructure Approach to Improving Effectiveness of Android UI Testing Tools
Wenyu Wang, Wing Lam, and Tao Xie
(University of Illinois at Urbana-Champaign, USA; Peking University, China)

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DialTest: Automated Testing for Recurrent-Neural-Network-Driven Dialogue Systems
Zixi Liu, Yang Feng, and Zhenyu Chen
(Nanjing University, China)

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GUIDER: GUI Structure and Vision Co-guided Test Script Repair for Android Apps
Tongtong Xu, Minxue Pan, Yu Pei, Guiyin Li, Xia Zeng, Tian Zhang, Yuetang Deng, and Xuandong Li
(Nanjing University, China; Hong Kong Polytechnic University, China; Tencent, China)

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Conditionally Accepted Papers

RAProducer: Efficiently Diagnose and Reproduce Data Race Bugs for Binaries via Trace Analysis
Ming Yuan, Yeseop Lee, Chao Zhang, Yun Li, Yan Cai, and Bodong Zhao
(Tsinghua University, China; Institute of Software at Chinese Academy of Sciences, China)

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Validating Static Warnings via Testing Code Fragments
Ashwin Kallingal Joshy, Xueyuan Chen, Benjamin Steenhoek, and Wei Le
(Iowa State University, USA)

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HomDroid: Detecting Android Covert Malware by Social-Network Homophily Analysis
Yueming Wu, Deqing Zou, Wei Yang, Xiang Li, and Hai Jin
(Huazhong University of Science and Technology, China; University of Texas at Dallas, USA)

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Optimizing Regression Testing in Continuous Integration by Exploiting Readily Available Information
Daniel Elsner, Florian Hauer, Alexander Pretschner, and Silke Reimer
(TU Munich, Germany; IVU Traffic Technologies, Germany)

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UAFSan: An Object-Identifier-Based Dynamic Approach for Detecting Use-After-Free Vulnerabilities
Binfa Gui, Wei Song, and Jeff Huang
(Nanjing University of Science and Technology, China; Texas A&M University, USA)

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Modular Call Graph Construction for Security Scanning of Node.js Applications
Benjamin Barslev Nielsen, Martin Toldam Torp, and Anders MøllerORCID logo
(Aarhus University, Denmark)

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Empirical Evaluation of Smart Contract Testing: What Is the Best Choice?
Meng Ren, Zijing Yin, Fuchen Ma, Zhenyang Xu, Yu Jiang, Chengnian Sun, Huizhong Li, and Yan Cai
(Tsinghua University, China; University of Waterloo, Canada; WeBank, n.n.; Institute of Software at Chinese Academy of Sciences, China)

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Identifying Privacy Weaknesses from Multi-party Trigger-Action Integration Platforms
Kulani Tharaka Mahadewa, Yanjun Zhang, Guangdong Bai, Lei Bu, Zhiqiang ZuoORCID logo, Dileepa Fernando, Zhenkai Liang, and Jin Song Dong
(National University of Singapore, Singapore; University of Queensland, Australia; Nanjing University, China)

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Demystifying VM-Based Packers for Android Apps
Lei Xue, Yuxiao Yan, Luyi Yan, Muhui Jiang, Xiapu Luo, Dinghao Wu, Yajin Zhou, and Zhiqiang Lin
(Hong Kong Polytechnic University, China; Xi'an Jiaotong University, China; Pennsylvania State University, USA; Zhejiang University, China; Ohio State University, USA)

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Continuous Test Suite Failure Prediction
Cong Pan and Michael Pradel
(Beihang University, China; University of Stuttgart, Germany)

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Toward Optimal MC/DC Test Case Generation
Sangharatna Godboley, Joxan Jaffar, Rasool Maghareh, and Arpita Dutta
(National Institute of Technology Warangal, India; National University of Singapore, Singapore; Huawei, Canada)

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iDEV: Exploring and Exploiting Semantic Deviations in ARM Instruction Processing
Shisong Qin, Chao Zhang, Kaixiang Chen, and Zheming Li
(Tsinghua University, China)

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Predoo: Precision Testing of Deep Learning Operators
Xufan Zhang, Ning Sun, Chunrong Fang, Jiawei Liu, Jia Liu, Dong Chai, Jiang Wang, and Zhenyu Chen
(Nanjing University, China; Huawei, n.n.)

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TERA: Optimizing Stochastic Regression Tests in Machine Learning Projects
Saikat Dutta, Jeeva Selvam, Aryaman Jain, and Sasa Misailovic
(University of Illinois at Urbana-Champaign, USA)

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Grammar-Agnostic Symbolic Execution by Token Symbolization
Weiyu Pan, Zhenbang Chen, Guofeng Zhang, Yunlai Luo, Yufeng Zhang, and Ji Wang
(National University of Defense Technology, China; Hunan University, China)

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