Feedback-Directed Random Test Generation

By Carlos Pacheco, Shuvendu K. Lahiri, Michael D. Ernst, Thomas Ball. International Conference on Software Engineering, 2007

944 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/6dc7017e357767dbc210bcb27080260fb0816701

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Citations in the corpus

Pynguin: Automated Unit Test Generation for Python (2022, methodology): “We designed these components with evolutionary algorithms in mind; one can of course also define purely random-based algorithms as we have done with our Random algorithm, based on the Randoop algorithm [15].”
RESTler: Stateful REST API Fuzzing (2019, methodology): “RESTler ’s dependency analysis is also related to the analysis of type dependencies performed by the Randoop algorithm [32] for typed object-oriented programs.”
Fuzzing: A Survey for Roadmap (2022, background): “If code coverage is not available, an intuitive solution is to retain seeds based on execution outputs, such as the legality of execution results [141] or state machine of protocol implementations [154].”
The Art, Science, and Engineering of Fuzzing: A Survey (2018, neutral): “Randoop [172] also uses a similar approach, though it was not designed specifically for finding vulnerabilities.”
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): “Pacheco et al. present a feedback-directed random test generation technique [37–39].”
CodeT: Code Generation with Generated Tests (2022, background): “Early works including Ran-doop (Pacheco et al., 2007), EvoSuite (Fraser & Arcuri, 2011), MOSA (Panichella et al., 2015), DynaMOSA (Panichella et al., 2017), and MIO (Arcuri, 2017), were proposed to automatically generate test cases for statically typed programming languages like Java.”
Automated Unit Test Generation for Python (2020, methodology): “Closest to our work is whole-suite test generation in EvoSuite [9] and feedbackdirected random test generation in Randoop [15].”
Automatic patch generation learned from human-written patches (2013, methodology): “Since the imbalance in test cases may lead to inaccurate patch evaluation, we will develop failing-test generation techniques by leveraging existing automatic testing techniques [31], [32], [20].”
Android permissions demystified (2011, methodology): “Applications are written in Java (possibly accompanied by native code), and each application runs in its own virtual machine.”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, background): “For example, assume one wants to evaluate a new tool for automatically generating unit tests for object-oriented software (e.g., Pex [113], Randoop [93] or EvoSuite [40]).”
Software Testing With Large Language Models: Survey, Landscape, and Vision (2023, neutral): “Although various approaches, including search-based [3], [4], constraint-based [5] or random-based [6] techniques to generate a suite of unit tests, the coverage and the meaningfulness of the generated tests are still far from satisfactory [7], [8].”
Randomized Differential Testing as a Prelude to Formal Verification (2007, methodology): “for testing object-oriented libraries using feedback-directed random testing [13].”
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation (2023, methodology): “Traditional Techniques Traditional techniques incorporate search-based [56], random-based [14], or constraint-based”
Type test scripts for TypeScript testing (2017, methodology): “Unlike the Randoop tool from the original work on feedback-directed random testing [Pacheco et al. 2007], our tool TStest does not directly perform this process but generates a script, called a type test script , that is specialized to the declaration file and performs the testing when executed.”
Pex-White Box Test Generation for .NET (2008, methodology): “Randoop [23] is a tool that generates new test-cases by composing previously found test-case fragments, supplying random input data.”
Are mutants a valid substitute for real faults in software testing? (2014, methodology): “We used three test generation tools for our study: EvoSuite [12], Randoop [31], and JCrasher [6].”