Randoop: feedback-directed random testing for Java

By Carlos Pacheco, Michael D. Ernst. Conference on Object-Oriented Programming Systems, Languages, and Applications, 2007

689 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/feebec1b65cb45de2abcb787d22cf24acf86f51a

How researchers see this paper

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

In Information Systems

1. Neel Sundaresan
2. Mark Harman
3. Shin Yoo
4. Alexey Svyatkovskiy
5. Michele Tufano
6. Shao Kun Deng
7. Dawn Drain
8. Zhendong Su

In Software

1. Mark Harman
2. Neel Sundaresan
3. Shin Yoo
4. Alexey Svyatkovskiy
5. Shao Kun Deng
6. Michele Tufano
7. Zhendong Su
8. Dawn Drain

Citations in the corpus

• Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation (2023, neutral): “Previous research on unit test generation has employed evolutionary algorithms, leading to tools such as EvoSuite [15] and Randoop [27], and several notable machine learning models [47, 3, 19, 13, 40, 39, 49, 51, 12, 28, 14, 34, 18].”
• 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].”
• 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].”
• Large Language Models for Software Engineering: Survey and Open Problems (2023, methodology): “For example, Bareiß et al. [97] reported an increase from the 10% achieved using Randoop [98] to 14% with CodeX, while Siddiq et al. [99] reported 80% coverage of the HumanEval dataset using CodeX, but also found that neither CodeX, nor CodeGen, nor GPT-3.”
• Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction (2022, methodology): “The advent of the object-oriented programming paradigm caused a shift in test input generation techniques, moving from primitive value exploration to deriving method sequences to maximize coverage [3], [4].”
• METHODS2TEST: A dataset of focal methods mapped to test cases (2022, methodology): “Specifically, in the context of code generation, researchers have proposed several techniques which automate the synthesis of unit test cases, such as EvoSuite [10], Randoop [17], and Agitar [3].”
• An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation (2023, methodology): “They position their technique as a complement to traditional techniques [8], [17].”
• FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (2017, neutral): “Randoop [41] automatically generates test cases for object oriented-programs through feedback-directed random test generation.”
• Unit Test Case Generation with Transformers (2020, background): “EvoSuite [6], Randoop [7], and Agitar [8] are among the most popular and widely used examples of such techniques.”