CUTE and jCUTE: Concolic Unit Testing and Explicit Path Model-Checking Tools

By Koushik Sen, Gul A. Agha. International Conference on Computer Aided Verification, 2006

477 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Software

View Paper: https://www.semanticscholar.org/paper/1c0c851e96fa13fc45e3298f4715f5d5d0a97e41

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Software

Citations in the corpus

A survey of the use of crowdsourcing in software engineering (2017, background): “Chen and Kim [166] investigated object mutation and constraint solving issues, underlying existing test generation tools such as jCUTE [228], Randoop [229] and Pex [230].”
Guided differential testing of certificate validation in SSL/TLS implementations (2015, methodology): “Dynamic symbolic execution techniques, such as DART [29], CUTE [43], and Pex [44], improve the effectiveness of symbolic execution and random testing by dynamically analyzing the program behaviors under random testing and automatically generating new test inputs to direct the execution along…”
Feedback-Directed Random Test Generation (2007, methodology): “Many techniques have been proposed to systematically explore method sequences [30, 4, 31, 12, 25, 5, 28].”
FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (2017, background): “While many individual test inputs generated by fuzz testing may be garbage, due to its low computational overhead, fuzz testing can generate test inputs much faster than more sophisticated methods such as dynamic symbolic execution [11, 17, 37, 38].”
Pex-White Box Test Generation for .NET (2008, methodology): “Most earlier approach to dynamic symbolic execution [12,27,26,6] in fact only use information from the last execution path to determine test inputs that will exercise the next path.”
Differential symbolic execution (2008, methodology): “The semantics ofexceptionthrowsinJavais handledby interpreting,forexample, getfield bytecodes as branching statements with an implicit test for nullness of the object being dereferenced.”
Fairness testing: testing software for discrimination (2017, methodology): “Automated testing research has produced tools to generate tests, including random testing, such as Randoop [63, 64], NightHawk [4], JCrasher [20], CarFast [65], and T3 [69]; search-based testing, such as EvoSuite [29], TestFul [9], and eToc [78]; dynamic symbolic execution tools, such as DSC [37], S”
A Survey of Symbolic Execution Techniques (2016, unknown): “This is the approach taken by a number of works (e.g., BitBlaze [98], [102], BAP [17], and [103]).”