17,370 research outputs found
Towards Smart Hybrid Fuzzing for Smart Contracts
Smart contracts are Turing-complete programs that are executed across a
blockchain network. Unlike traditional programs, once deployed they cannot be
modified. As smart contracts become more popular and carry more value, they
become more of an interesting target for attackers. In recent years, smart
contracts suffered major exploits, costing millions of dollars, due to
programming errors. As a result, a variety of tools for detecting bugs has been
proposed. However, majority of these tools often yield many false positives due
to over-approximation or poor code coverage due to complex path constraints.
Fuzzing or fuzz testing is a popular and effective software testing technique.
However, traditional fuzzers tend to be more effective towards finding shallow
bugs and less effective in finding bugs that lie deeper in the execution. In
this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary
fuzzing with constraint solving in order to execute more code and find more
bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts
of a smart contract, while constraint solving is used to generate inputs which
satisfy complex conditions that prevent the evolutionary fuzzing from exploring
deeper paths. Moreover, we use data dependency analysis to efficiently generate
sequences of transactions, that create specific contract states in which bugs
may be hidden. We evaluate the effectiveness of our fuzzing strategy, by
comparing CONFUZZIUS with state-of-the-art symbolic execution tools and
fuzzers. Our evaluation shows that our hybrid fuzzing approach produces
significantly better results than state-of-the-art symbolic execution tools and
fuzzers
Exact Gap Computation for Code Coverage Metrics in ISO-C
Test generation and test data selection are difficult tasks for model based
testing. Tests for a program can be meld to a test suite. A lot of research is
done to quantify the quality and improve a test suite. Code coverage metrics
estimate the quality of a test suite. This quality is fine, if the code
coverage value is high or 100%. Unfortunately it might be impossible to achieve
100% code coverage because of dead code for example. There is a gap between the
feasible and theoretical maximal possible code coverage value. Our review of
the research indicates, none of current research is concerned with exact gap
computation. This paper presents a framework to compute such gaps exactly in an
ISO-C compatible semantic and similar languages. We describe an efficient
approximation of the gap in all the other cases. Thus, a tester can decide if
more tests might be able or necessary to achieve better coverage.Comment: In Proceedings MBT 2012, arXiv:1202.582
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