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Fine-grained Coverage-based Fuzzing

https://doi.org/10.1145/3587158
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-09099-3_4openalex-snapshot
An All-in-One Toolkit for Automated White-Box Testing
inferredcites →https://doi.org/10.1007/s10009-020-00567-yopenalex-snapshot
Comparing mutation coverage against branch coverage in an industrial setting
inferredcites →https://doi.org/10.1016/j.scico.2021.102641openalex-snapshot
Specify and measure, cover and reveal: A unified framework for automated test generation
inferredcites →https://doi.org/10.1017/9781316771273openalex-snapshot
Introduction to Software Testing
inferredcites →https://doi.org/10.1017/cbo9780511809163openalex-snapshot
Introduction to Software Testing
inferredcites →https://doi.org/10.1109/32.286422openalex-snapshot
An empirical evaluation of weak mutation
inferredcites →https://doi.org/10.1109/c-m.1978.218136openalex-snapshot
Hints on Test Data Selection: Help for the Practicing Programmer
inferredcites →https://doi.org/10.1109/eurosp53844.2022.00026openalex-snapshot
Fuzzing with Data Dependency Information
inferredcites →https://doi.org/10.1109/icse.2015.103openalex-snapshot
Trivial Compiler Equivalence: A Large Scale Empirical Study of a Simple, Fast and Effective Equivalent Mutant Detection Technique
inferredcites →https://doi.org/10.1109/icsm.2010.5609672openalex-snapshot
Test generation via Dynamic Symbolic Execution for mutation testing
inferredcites →https://doi.org/10.1109/icst.2014.30openalex-snapshot
Efficient Leveraging of Symbolic Execution to Advanced Coverage Criteria
inferredcites →https://doi.org/10.1109/icst.2019.00015openalex-snapshot
MemFuzz: Using Memory Accesses to Guide Fuzzing
inferredcites →https://doi.org/10.1109/icst39641.2017openalex-snapshot
2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)
inferredcites →https://doi.org/10.1109/issre.2003.1251034openalex-snapshot
Coverage criteria for logical expressions
inferredcites →https://doi.org/10.1109/sp.2016.15openalex-snapshot
LAVA: Large-Scale Automated Vulnerability Addition
inferredcites →https://doi.org/10.1109/sp40000.2020.00117openalex-snapshot
Ijon: Exploring Deep State Spaces via Fuzzing
inferredcites →https://doi.org/10.1109/tse.2019.2946563openalex-snapshot
The Art, Science, and Engineering of Fuzzing: A Survey
inferredcites →https://doi.org/10.1145/1985793.1985795openalex-snapshot
A practical guide for using statistical tests to assess randomized algorithms in software engineering
inferredcites →https://doi.org/10.1145/3106237.3106295openalex-snapshot
Steelix: program-state based binary fuzzing
inferredcites →https://doi.org/10.1145/3180155.3180191openalex-snapshot
Time to clean your test objectives
inferredcites →https://doi.org/10.1145/3360600openalex-snapshot
FuzzFactory: domain-specific fuzzing with waypoints
inferredcites →https://doi.org/10.1145/3377811.3380421openalex-snapshot
Ankou
inferredcites →https://doi.org/10.1145/3428334openalex-snapshot
Magma
inferredcites →https://doi.org/10.1145/3545258.3545285openalex-snapshot
Investigating Coverage Guided Fuzzing with Mutation Testing
inferredcites →https://doi.org/10.1145/96267.96279openalex-snapshot
An empirical study of the reliability of UNIX utilities
inferredcites →https://doi.org/10.1186/s42400-018-0002-yopenalex-snapshot
Fuzzing: a survey
inferredcites →https://doi.org/10.14722/ndss.2016.23368openalex-snapshot
Driller: Augmenting Fuzzing Through Selective Symbolic Execution
inferredcites →https://doi.org/10.14722/ndss.2017.23404openalex-snapshot
VUzzer: Application-aware Evolutionary Fuzzing
inferredcites →https://doi.org/10.14722/ndss.2019.23371openalex-snapshot
REDQUEEN: Fuzzing with Input-to-State Correspondence
inferredcites →https://doi.org/10.14722/ndss.2021.24486openalex-snapshot
Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing
inferredcites →https://doi.org/10.5555/1855741.1855756openalex-snapshot
KLEE: unassisted and automatic generation of high-coverage tests for complex systems programs

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.cose.2023.103354openalex-snapshot
FCEVAL: An effective and quantitative platform for evaluating fuzzer combinations fairly and easily
inferred← citeshttps://doi.org/10.1109/acsac67867.2025.00017openalex-snapshot
Sagitta: Facilitating Post-Fuzzing Root Cause Analysis via Data Flow Differencing
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00145openalex-snapshot
Function Clustering-Based Fuzzing Termination: Toward Smarter Early Stopping
inferred← citeshttps://doi.org/10.1145/3605157.3605172openalex-snapshot
DiPri: Distance-Based Seed Prioritization for Greybox Fuzzing (Registered Report)
inferred← citeshttps://doi.org/10.1145/3678722.3685530openalex-snapshot
Visualization Task Taxonomy to Understand the Fuzzing Internals (Registered Report)
inferred← citeshttps://doi.org/10.1145/3691620.3695284openalex-snapshot
Visualizing and Understanding the Internals of Fuzzing
inferred← citeshttps://doi.org/10.1145/3713081.3731723openalex-snapshot
On the Applicability of Benford’s Law to Detect Saturation in Fuzzing (Registered Report)
inferred← citeshttps://doi.org/10.1145/3718346openalex-snapshot
Visualization Task Taxonomy to Understand the Fuzzing Internals
inferred← citeshttps://doi.org/10.1145/3832160openalex-snapshot
Fuzzing FPGA Synthesis and Simulation Tools via LLM-Generated Syntax-Valid HDL Codes
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4963553/v1openalex-snapshot
Systematic Review of Fuzzing in IoT: Evaluating Techniques, Vulnerabilities, and Research Gaps
inferred← citeshttps://doi.org/10.3390/electronics13244962openalex-snapshot
Reinforcement Learning-Based Multi-Phase Seed Scheduling for Network Protocol Fuzzing
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