SpecFuzzer: A Tool for Inferring Class Specifications via Grammar-based Fuzzing
In object-oriented design, class specifications are primarily used to express properties describing the intended behavior of the class methods and constraints on class’ objects. Although the presence of these specifications is important for various software engineering tasks such as test generation, bug finding and automated debugging, developers rarely write them.
In this tool demo we present the details of SpecFuzzer, a tool that aims at alleviating the problem of writing class specifications by using a combination of grammar-based fuzzing, dynamic invariant detection and mutation analysis to automatically infer specifications for Java classes. Given a class under analysis, SpecFuzzer uses (i) a generator of candidate assertions derived from a grammar automatically extracted from the class; (ii) a dynamic invariant detector –Daikon– in order to discard the assertions invalidated by a test suite; and (iii) a mutation-based mechanism to cluster and rank assertions, so that similar constraints are grouped and the stronger ones prioritized.
| Slides (specfuzzer-tool-ase2023.pdf) | 3.90MiB |
Thu 14 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
15:30 - 17:00 | FuzzingNIER Track / Journal-first Papers / Research Papers / Tool Demonstrations at Plenary Room 2 Chair(s): Lars Grunske Humboldt-Universität zu Berlin | ||
15:30 12mTalk | Fine-Grained Coverage-Based Fuzzing Journal-first Papers Wei-Cheng Wu University of Southern California, USA, Bernard Nongpoh CEA LIST, University Paris-Saclay, Marwan Nour CEA, LIST, Université Paris Saclay, Michaël Marcozzi CEA, LIST, Université Paris Saclay, Sébastien Bardin CEA LIST, University Paris-Saclay, Christophe Hauser Dartmouth College Link to publication File Attached | ||
15:42 12mTalk | MLIRSmith: Random Program Generation for Fuzzing MLIR Compiler Infrastructure Research Papers Haoyu Wang College of Intelligence and Computing, Tianjin University, Junjie Chen Tianjin University, Chuyue Xie College of Intelligence and Computing, Tianjin University, Shuang Liu Tianjin University, Zan Wang Tianjin University, Qingchao Shen Tianjin University, Yingquan Zhao Tianjin University Pre-print File Attached | ||
15:54 12mTalk | Thunderkaller: Profiling and Improving the Performance of Syzkaller Research Papers Yang Lan Institute for Network Science and Cyberspace of Tsinghua University, Di Jin Brown University, Zhun Wang Institute for Network Science and Cyberspace of Tsinghua University, Wende Tan Tsinghua University, Zheyu Ma Tsinghua University, Chao Zhang Tsinghua University File Attached | ||
16:06 12mTalk | PHYFU: Fuzzing Modern Physics Simulation Engines Research Papers Dongwei Xiao Hong Kong University of Science and Technology, Zhibo Liu Hong Kong University of Science and Technology, Shuai Wang Hong Kong University of Science and Technology Link to publication DOI | ||
16:18 12mTalk | NaturalFuzz: Natural Input Generation for Big Data Analytics Research Papers Ahmad Humayun Virginia Tech, Yaoxuan Wu UCLA, Miryung Kim University of California at Los Angeles, USA, Muhammad Ali Gulzar Virginia Tech File Attached | ||
16:30 12mTalk | SpecFuzzer: A Tool for Inferring Class Specifications via Grammar-based Fuzzing Tool Demonstrations Facundo Molina IMDEA Software Institute, Marcelo d'Amorim North Carolina State University, Nazareno Aguirre University of Rio Cuarto and CONICET, Argentina Pre-print Media Attached File Attached | ||
16:42 12mTalk | Scalable Industrial Control System Analysis via XAI-based Gray-Box Fuzzing NIER Track Justin Kur Oakland University, Jingshu Chen Oakland University, Jun Huang City University of Hong Kong | ||