EvoSuite: automatic test suite generation for object-oriented software

By Gordon Fraser, Andrea Arcuri. 2011

1,321 citations recorded by the source corpus.

Subfields: Information Systems, Software, Natural Language Processing

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Citations in the corpus

InCoder: A Generative Model for Code Infilling and Synthesis (2022, methodology): “This includes methods to infer variable types [50, 66], generate unit tests [25], repair programs [30, 70, 20, 71], and verify program correctness [55].”
Unit Test Case Generation with Transformers (2020, methodology): “EvoSuite [6], Randoop [7], and Agitar [8] are among the most popular and widely used examples of such techniques.”
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.”
An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation (2023, neutral): “This fact has inspired extensive research on techniques for automated test generation including fuzzing [5], [6], feedback-directed random test generation [7], [8], [9], [10], [11], dynamic symbolic execution [12], [13], [14], [15], and search-based and evolutionary techniques [16], [17].”
Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models (2022, methodology): “CodaMosa [41] combines traditional search-based software testing (SBST) [19, 20] with LLMs (e.”
Genetic programming for Reverse Engineering (2013, methodology): “If there were not, then search based test data generation tools [3], [26], [41], [57] can be used to enhance the test suite.”
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks (2021, background): “Watson et al. [16] start from the work by Shamshiri et al. [39], who observed that tools for the automatic generation of test cases such as Evosuite [40], Randoop [41] and Agitar [42] exhibit insufﬁciencies in the automatically generated assert statements.”
On Learning Meaningful Assert Statements for Unit Test Cases (2020, methodology): “usages for ATLAS. First, it can be used as a code completion mechanism when manually writing unit tests in the IDE. Second, it could be combined with existing tools for automatic test case generation [3, 10, 24]. We currently lack empirical evidence to substantiate any claim on the effectiveness of ”
FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (2017, methodology): “Evosuite [19] uses seed inputs and genetic algorithms to achieve high code coverage.”
Automated Unit Test Generation for Python (2020, neutral): “Established techniques such as feedback-directed random test generation [15] or evolutionary algorithms [7] are implemented in mature research prototypes, but these are based on strong assumptions on the availability of static type information, as is the case in statically typed languages like Java.”
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].”
Chapter Six - Mutation Testing Advances: An Analysis and Survey (2019, methodology): “Evosuite [232] adopts this practice for generating test oracles for Java programs.”
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation (2023, methodology): “CodeMosa [26] is a code coverage-based technique that equips traditional search-based software testing (SBST [15]) with CODEX for better unit test generation.”
No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation (2023, methodology): “For example, Evosuite [56], one of the most representative searchbased techniques, leverages the evolutionary algorithm to automatically generate test suites for the given Java classes with the goal of maximizing the coverage.”
Leveraging Automated Unit Tests for Unsupervised Code Translation (2021, methodology): “There are now several test data generation tools for languages, including C (Cadar et al., 2008; Lakhotia et al., 2013) and Java (Fraser & Arcuri, 2011).”