Whole Test Suite Generation

By Gordon Fraser, Andrea Arcuri. IEEE Transactions on Software Engineering, 2013

628 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/39e6029da19a357449b48e2f6d9c8b35a802e7c4

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Citations in the corpus

Automated Unit Test Generation for Python (2020, methodology): “s.Theeﬀectivenessofrandomtestgeneratorscanbeincreasedbyintegrating heuristics [13,17]. Search-based approaches use a similar representation, but applyevolutionarysearchalgorithmstomaximizecodecoverage[1,4,9,19]. As an example to illustrate how type information is used by existing test generators,con”
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.”
DeepMutation: Mutation Testing of Deep Learning Systems (2018, background): “Besides the design of mutation operators, great efforts have also been devoted to other key issues of mutation testing, such as theoretical aspects [57]–[59] of mutation testing, performance enhancement [7], [16], [60]–[62], platform and tool support [30], [63]–[65], as well as more general mutation”
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”
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].”
Sapienz: multi-objective automated testing for Android applications (2016, background): “SBSE representation: Sapienz performs the whole test suite evolution [13, 33] thus each individual corresponds to a test suite.”
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems (2018, background): “, statement coverage, branch coverage) towards the exploration of diverse software states for defect detection has become the de facto standard in traditional software testing [5, 16, 17, 33], the study to design suitable testing criteria for DL is desperately demanding.”
Parameter tuning or default values? An empirical investigation in search-based software engineering (2013, methodology): “Our first case study is a subset of 20 Java classes out of those previously used to evaluate EvoSuite (Fraser and Arcuri 2013).”
Pynguin: Automated Unit Test Generation for Python (2022, methodology): “The five search-based algorithms, however, only show small differences, with DynaMOSA achieving the highest and Whole Suite the lowest coverage values (mean branch coverage for DynaMOSA: 68.0 %, MIO: 67.0 %, MOSA: 67.8 %, Random: 63.6 %, Whole Suite: 66.9 %, and for Whole Suite with archive: 67.5 %)”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, methodology): “The whole-suite (WS) approach [3], [16] to test case”
Themis: automatically testing software for discrimination (2018, methodology): “Unlike other automated test generation tools, e.g., Randoop [25] and EvoSuite [8], Themis processes test results to compute discrimination scores.”
A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering (2014, background): “Without formal proofs (e.g., as done by Fraser and Arcuri [40]), it is not safe to speak about the existence of the mean in those cases.”
Automated Program Repair in the Era of Large Pre-trained Language Models (2022, neutral): “LLMs can also be used to target more context dependent tasks such as test [85] or test-oracle [86] generation where the input can be the focal method we want to generate unit test for.”
Guided differential testing of certificate validation in SSL/TLS implementations (2015, methodology): “Fraser and Arcuri introduce a technique called whole test suite generation that can evolve all the test cases in a test suite simultaneously [26].”
The Oracle Problem in Software Testing: A Survey (2015, methodology): “Fraser and Arcuri [65] generate test suites for Java using their EvoSuite tool.”