Symstra: A Framework for Generating Object-Oriented Unit Tests Using Symbolic Execution

By Tao Xie, Darko Marinov, Wolfram Schulte, David Notkin. International Conference on Tools and Algorithms for Construction and Analysis of Systems, 2005

309 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Software

View Paper: https://www.semanticscholar.org/paper/3b99ed6f49e7d2837f9f2cba0c7f11d7fa9316c3

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Citations in the corpus

Mutation-Driven Generation of Unit Tests and Oracles (2010, methodology): “This idea has been implemented in tools like DART [14] and CUTE [31], and is also applied in Microsoft’s parametrized unit testing tool PEX [33] or the object-oriented testing framework Symstra [39].”
Heuristics for Scalable Dynamic Test Generation (2008, methodology): “Several symbolic techniques for automated test generation [18, 5, 28, 27, 1, 29] have been proposed to address the limitations of manual and random testing.”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, methodology): “Other than evolutionary approaches, other techniques have been proposed in the literature for test case and test data generation, such as dynamic symbolic execution [7], [26], [53], [56] and random testing [9], [32], [36], [37].”
Combining unit-level symbolic execution and system-level concrete execution for testing nasa software (2008, methodology): “Symstra [34] uses a specialized symbolic execution over numeric data for generating test sequences for (sequential) Java containers.”
Feedback-Directed Random Test Generation (2007, methodology): “Symstra: A framework for generating object-oriented unit tests using symbolic execution.”
Concolic Testing for Deep Neural Networks (2018, background): “In symbolic execution [5, 26, 32], an execution path is encoded symbolically.”
CUTE: a concolic unit testing engine for C (2005, methodology): “, red-black trees, there are memory graphs that cannot be constructed through additions only but require removals [27,30].”
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”
DART: directed automated random testing (2005, background): “There is a rich literature on test-vector generation using symbolic execution (e.g., see [21, 27, 10, 3, 36, 38, 9]).”
Hybrid Concolic Testing (2007, methodology): “In particular, we adopted the approach of generating data structures using a sequence of function calls [28, 33].”