ISSTA 2019
28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019)
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28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019), July 15–19, 2019, Beijing, China

ISSTA 2019 – Preliminary Table of Contents

Contents - Abstracts - Authors

Frontmatter

Title Page

Message from the Chairs

ISSTA 2019 Organization

Sponsors

Papers

Regression Event Selection for Android Applications
Aman Sharma and Rupesh Nasre
(IIT Madras, India)

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Improving Random GUI Testing with Image-Based Widget Detection
Thomas D. White, Gordon Fraser, and Guy J. Brown
(University of Sheffield, UK; University of Passau, Germany)

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Effective and Efficient API Misuse Detection via Exception Propagation and Search-Based Testing
Maria Kechagia, Xavier Devroey, Annibale Panichella, Georgios Gousios, and Arie van Deursen
(Delft University of Technology, Netherlands)

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Judge: Identifying, Understanding, and Evaluating Sources of Unsoundness in Call Graphs
Michael Reif, Florian Kübler, Michael Eichberg, Dominik Helm, and Mira Mezini
(TU Darmstadt, Germany)

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Codebase-Adaptive Detection of Security-Relevant Methods
Goran Piskachev, Lisa Nguyen Quang Do, and Eric Bodden
(Fraunhofer IEM, Germany; University of Paderborn, Germany)

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SARA: Self-Replay Augmented Record and Replay for Android in Industrial Cases
Jiaqi Guo, Shuyue Li, Jian-Guang Lou, Zijiang Yang, and Ting Liu
(Xi'an Jiaotong University, China; Microsoft Research, China; Western Michigan University, USA)

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Crash-Avoiding Program Repair
Xiang Gao, Sergey Mechtaev, and Abhik Roychoudhury
(National University of Singapore, Singapore; University College London, UK)

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Automated Program Repair via Bytecode Mutation: An Extensive Study
Ali Ghanbari, Samuel Benton, and Lingming Zhang
(University of Texas at Dallas, USA)

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Exploiting the Laws of Order in Smart Contracts
Aashish Kolluri, Ivica Nikolic, Ilya Sergey, Aquinas Hobor, and Prateek Saxena
(National University of Singapore, Singapore; Yale-NUS College, Singapore)

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Failure Clustering without Coverage
Mojdeh Golagha, Constantin Lehnhoff, Alexander Pretschner, and Hermann Ilmberger
(TU Munich, Germany; BMW, Germany)

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Adlib: Analyzer for Mobile Ad Platform Libraries
SungHo Lee and Sukyoung Ryu
(KAIST, South Korea)

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LibID: Reliable Identification of Obfuscated Third-Party Android Libraries
Jiexin Zhang, Alastair R. Beresford, and Stephan A. Kollmann
(University of Cambridge, UK)

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A Large-Scale Study of Application Incompatibilities in Android
Haipeng Cai, Ziyi Zhang, Li Li, and Xiaoqin Fu
(Washington State University, USA; Monash University, Australia)
The rapid expansion of the Android ecosystem is accompanied by continuing diversification of platforms and devices, resulting in increasing incompatibility issues which damage user experiences and impede app development productivity. In this paper, we conducted a large-scale, longitudinal study of compatibility issues in 62,894 benign apps developed in the past eight years, to understand the symptoms and causes of these issues. We further investigated the incompatibilities that are actually exercised at runtime through the system logs and execution traces of 15,045 apps. Our study revealed that, among others, (1) compatibility issues were prevalent and persistent at both installation and run time, with greater prevalence of run-time incompatibilities, (2) there were no certain Android versions that consistently saw more or less app incompatibilities than others, (3) installation-time incompatibilities were strongly correlated with the minSdkVersion specified in apps, while run-time incompatibilities were most significantly correlated with the underlying platform’s API level, and (4) installation-time incompatibilities were mostly due to apps’ use of architecture-incompatible native libraries, while run-time incompatibilities were mostly due to API changes during SDK evolution. We offered further insights into app incompatibilities, as well as recommendations on dealing with the issues for bother developers and end users of Android apps.
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Optimal Context-Sensitive Dynamic Partial Order Reduction with Observers
Elvira Albert, Maria Garcia de la Banda, Miguel Gómez-Zamalloa, Miguel Isabel, and Peter J. Stuckey
(Complutense University of Madrid, Spain; Monash University, Australia)

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Automatically Testing Self-Driving Cars with Search-Based Procedural Content Generation
Alessio Gambi, Marc Mueller, and Gordon Fraser
(University of Passau, Germany; BeamNG, Germany)

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Interactive Metamorphic Testing of Debuggers
Sandro Tolksdorf, Daniel Lehmann, and Michael Pradel
(TU Darmstadt, Germany)

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Mitigating the Effects of Flaky Tests on Mutation Testing
August Shi, Jonathan Bell, and Darko Marinov
(University of Illinois at Urbana-Champaign, USA; George Mason University, USA)

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Learning User Interface Element Interactions
Christian Degott, Nataniel P. Borges Jr., and Andreas Zeller
(CISPA, Germany)

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Root Causing Flaky Tests in a Large-Scale Industrial Setting
Wing Lam, Patrice Godefroid, Suman Nath, Anirudh Santhiar, and Suresh Thummalapenta
(University of Illinois at Urbana-Champaign, USA; Microsoft, n.n.)

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Automated API-Usage Update for Android Apps
Mattia Fazzini, Qi Xin, and Alessandro Orso
(Georgia Institute of Technology, USA)

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Mining Android Crash Fixes in the Absence of Issue- and Change-Tracking Systems
Pingfan Kong, Li Li, Jun Gao, Tegawendé F. Bissyandé, and Jacques Klein
(University of Luxembourg, Luxembourg; Monash University, Australia)

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Assessing the State and Improving the Art of Parallel Testing for C
Oliver Schwahn, Nicolas Coppik, Stefan Winter, and Neeraj Suri
(TU Darmstadt, Germany)

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DeepFL: Integrating Multiple Fault Diagnosis Dimensions for Deep Fault Localization
Xia Li, Wei Li, Yuqun Zhang, and Lingming Zhang
(University of Texas at Dallas, USA; Southern University of Science and Technology, China)

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TestMig: Migrating GUI Test Cases from iOS to Android
Xue Qin, Hao Zhong, and Xiaoyin Wang
(University of Texas at San Antonio, USA; Shanghai Jiao Tong University, China)

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Semantic Fuzzing with Zest
Rohan Padhye, Caroline Lemieux, Koushik Sen, Mike Papadakis, and Yves Le Traon
(University of California at Berkeley, USA; University of Luxembourg, Luxembourg)

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History-Driven Build Failure Fixing: How Far Are We?
Yiling Lou, Junjie Chen, Lingming Zhang, Dan Hao, and Lu Zhang
(Peking University, China; University of Texas at Dallas, USA)

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DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks
Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, and Simon See
(Nanyang Technological University, Singapore; Kyushu University, Japan; Carnegie Mellon University, USA; Macquarie University, Australia; Data61 at CSIRO, Australia; University of Illinois at Urbana-Champaign, USA; NVIDIA AI Tech Centre, n.n.)

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Search-Based Test and Improvement of Machine-Learning-Based Anomaly Detection Systems
Maxime Cordy, Steve Muller, Mike Papadakis, and Yves Le Traon
(University of Luxembourg, Luxembourg)

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Detecting Memory Errors at Runtime with Source-Level Instrumentation
Zhe Chen, Junqi Yan, Shuanglong Kan, Ju Qian, and Jingling Xue
(Nanjing University of Aeronautics and Astronautics, China; UNSW, Australia)

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