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To Infinity, and Beyond (Coverage)

Publications associées (27)

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DatAFLow: Toward a Data-Flow-Guided Fuzzer

Mathias Josef Payer

Coverage-guided greybox fuzzers rely on control-flow coverage feedback to explore a target program and uncover bugs. Compared to control-flow coverage, data-flow coverage offers a more fine-grained approximation of program behavior. Data-flow coverage capt ...

2023

Arvin: Greybox Fuzzing Using Approximate Dynamic CFG Analysis

Mathias Josef Payer, Sirus Shahini

Fuzzing has emerged as the most broadly used testing technique to discover bugs. Effective fuzzers rely on coverage to prioritize inputs that exercise new program areas. Edge-based code coverage of the Program Under Test (PUT) is the most commonly used cov ...

ASSOC COMPUTING MACHINERY2023

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Volkan Cevher

Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...

2023

DatAFLow: Toward a Data-flow-guided Fuzzer

Mathias Josef Payer

This Replicating Computational Report (RCR) describes (a) our datAFLow fuzzer and (b) how to replicate the results in "datAFLow: Toward a Data-Flow-Guided Fuzzer." Our primary artifact is the datAFLow fuzzer. Unlike traditional coverage-guided greybox fuzz ...

2023

Quantum conformance test

Elena Losero, Marco Genovese

We introduce a protocol addressing the conformance test problem, which consists in determining whether a process under test conforms to a reference one. We consider a process to be characterized by the set of end products it produces, which is generated ac ...

AMER ASSOC ADVANCEMENT SCIENCE2021

Seed Selection for Successful Fuzzing

Mathias Josef Payer

Mutation-based greybox fuzzing-unquestionably the most widely-used fuzzing technique-relies on a set of non-crashing seed inputs (a corpus) to bootstrap the bug-finding process. When evaluating a fuzzer, common approaches for constructing this corpus inclu ...

ASSOC COMPUTING MACHINERY2021

FuZZan: Efficient Sanitizer Metadata Design for Fuzzing

Mathias Josef Payer

Fuzzing is one of the most popular and effective techniques for finding software bugs. To detect triggered bugs, fuzzers leverage a variety of sanitizers in practice. Unfortunately, sanitizers target long running experiments-e.g., developer test suites-not ...

USENIX ASSOC2020

Decoding Cognitive States under Varying Difficulty Levels

Ping-Keng Jao

Understanding cognitive states of human under different difficulty levels is useful in improving human-human and human-machine interactions. For example, a crucial factor in designing games is maintaining the engagement of players. An ideal scenario would ...

EPFL2020

Discover deeper bugs with dynamic symbolic execution and coverage-based fuzz testing

George Candea, Vitaly Chipounov, Bin Zhang, Chao Feng

Coverage-based fuzz testing and dynamic symbolic execution are both popular program testing techniques. However, on their own, both techniques suffer from scalability problems when considering the complexity of modern software. Hybrid testing methods attem ...

INST ENGINEERING TECHNOLOGY-IET2018

deepschema.org: An Ontology for Typing Entities in the Web of Data

Karl Aberer, Michele Catasta, Panagiotis Smeros, Amit Gupta

Discovering the appropriate type of an entity in the Web of Data is still considered an open challenge, given the complexity of the many tasks it entails. Among them, the most notable is the definition of a generic and cross-domain ontology. While the onto ...

2017