research-article

MoFuzz: a fuzzer suite for testing model-driven software engineering tools

Authors:

Hoang Lam Nguyen,

Lars GrunskeAuthors Info & Claims

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

Pages 1103 - 1115

https://doi.org/10.1145/3324884.3416668

Published: 27 January 2021 Publication History

Abstract

Fuzzing or fuzz testing is an established technique that aims to discover unexpected program behavior (e.g., bugs, security vulnerabilities, or crashes) by feeding automatically generated data into a program under test. However, the application of fuzzing to test Model-Driven Software Engineering (MDSE) tools is still limited because of the difficulty of existing fuzzers to provide structured, well-typed inputs, namely models that conform to typing and consistency constraints induced by a given meta-model and underlying modeling framework. By drawing from recent advances on both fuzz testing and automated model generation, we present three different approaches for fuzzing MDSE tools: A graph grammar-based fuzzer and two variants of a coverage-guided mutation-based fuzzer working with different sets of model mutation operators. Our evaluation on a set of real-world MDSE tools shows that our approaches can outperform both standard fuzzers and model generators w.r.t. their fuzzing capabilities. Moreover, we found that each of our approaches comes with its own strengths and weaknesses in terms of fault finding capabilities and the ability to cover different aspects of the system under test. Thus the approaches complement each other, forming a fuzzer suite for testing MDSE tools.

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Cited By

Li FChen XXiao XSun XChen CWang SHan JFilkov VRay BZhou M(2024)Incremental Context-free Grammar Inference in Black Box SettingsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695494(1171-1182)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695494
Misu MAchar RLopes C(2024)SourcererJBF: A Java Build Framework For Large-Scale CompilationACM Transactions on Software Engineering and Methodology10.1145/363571033:3(1-35)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3635710
Arefin MShetiya SWang ZCsallner CRoychoudhury APaiva AAbreu RStorey M(2024)Fast Deterministic Black-box Context-free Grammar InferenceProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639214(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639214
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Index Terms

MoFuzz: a fuzzer suite for testing model-driven software engineering tools
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Software system structures
      1. Software system models
        Model-driven software engineering

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cover image ACM Conferences

ASE '20: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

December 2020

1449 pages

ISBN:9781450367684

DOI:10.1145/3324884

General Chair:
John Grundy,
Program Chairs:
Claire Le Goues,
David Lo

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Publication History

Published: 27 January 2021

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Funding Sources

German Research Foundation (DFG)

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ASE '20

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ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering

December 21 - 25, 2020

Virtual Event, Australia

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Li FChen XXiao XSun XChen CWang SHan JFilkov VRay BZhou M(2024)Incremental Context-free Grammar Inference in Black Box SettingsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695494(1171-1182)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695494
Misu MAchar RLopes C(2024)SourcererJBF: A Java Build Framework For Large-Scale CompilationACM Transactions on Software Engineering and Methodology10.1145/363571033:3(1-35)Online publication date: 15-Mar-2024
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Arefin MShetiya SWang ZCsallner CRoychoudhury APaiva AAbreu RStorey M(2024)Fast Deterministic Black-box Context-free Grammar InferenceProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639214(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639214
Wang JXu HXiao CZhang LZheng YWu XChang XZhang X(2024)Semantic Research on Model-Driven Code Generation2024 4th International Conference on Electronic Materials and Information Engineering (EMIE)10.1109/EMIE61984.2024.10616515(87-90)Online publication date: 12-Jun-2024
https://doi.org/10.1109/EMIE61984.2024.10616515
Karimi MKolahdouz-Rahimi STroya J(2024)Ant-colony optimization for automating test model generation in model transformation testingJournal of Systems and Software10.1016/j.jss.2023.111882208(111882)Online publication date: Feb-2024
https://doi.org/10.1016/j.jss.2023.111882
Vikram VLaybourn ILi ANair NOBrien KSanna RPadhye RJust RFraser G(2023)Guiding Greybox Fuzzing with Mutation TestingProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598107(929-941)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598107
Zhu XWen SCamtepe SXiang Y(2022)Fuzzing: A Survey for RoadmapACM Computing Surveys10.1145/351234554:11s(1-36)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3512345
Nguyen HGrunske LDwyer MDamian DZeller A(2022)BeDivFuzzProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510182(249-261)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510182
Zhang JCui ZChen XWu HZheng LLiu J(2022)DeltaFuzz: Historical Version Information Guided Fuzz TestingJournal of Computer Science and Technology10.1007/s11390-021-1663-737:1(29-49)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s11390-021-1663-7
Boll ABrokhausen FAmorim TKehrer TVogelsang A(2021)Characteristics, potentials, and limitations of open-source Simulink projects for empirical researchSoftware and Systems Modeling (SoSyM)10.1007/s10270-021-00883-020:6(2111-2130)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s10270-021-00883-0

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