Mutation Analysis: Answering the Fuzzing Challenge
January 27, 2022 Β· Declared Dead Β· π arXiv.org
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Authors
Rahul Gopinath, Philipp GΓΆrz, Alex Groce
arXiv ID
2201.11303
Category
cs.SE: Software Engineering
Cross-listed
cs.CR
Citations
13
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Fuzzing is one of the fastest growing fields in software testing. The idea behind fuzzing is to check the behavior of software against a large number of randomly generated inputs, trying to cover all interesting parts of the input space, while observing the tested software for anomalous behaviour. One of the biggest challenges facing fuzzer users is how to validate software behavior, and how to improve the quality of oracles used. While mutation analysis is the premier technique for evaluating the quality of software test oracles, mutation score is rarely used as a metric for evaluating fuzzer quality. Unless mutation analysis researchers can solve multiple problems that make applying mutation analysis to fuzzing challenging, mutation analysis may be permanently sidelined in one of the most important areas of testing and security research. This paper attempts to understand the main challenges in applying mutation analysis for evaluating fuzzers, so that researchers can focus on solving these challenges.
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