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Automatic test generation plays a critical role in software quality assurance.
Use of ranks in one-criterion variance analysis
William H Kruskal and W Allen Wallis. 1952 · 1952
Earlier work this paper cites.
Randoop: feedback-directed random testing for Java. In Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion . 815–816
Carlos Pacheco and Michael D Ernst. 2007 · 2007
Earlier work this paper cites.
Wilcoxon signed-rank test
Robert F Woolson. 2007 · 2007
Earlier work this paper cites.
Pex–white box test generation for. net. In International conference on tests and proofs . Springer, 134–153
Nikolai Tillmann and Jonathan De Halleux. 2008 · 2008
Earlier work this paper cites.
An analysis and survey of the development of mutation testing
Yue Jia and Mark Harman. 2010 · 2010
Earlier work this paper cites.
A practical guide for using statistical tests to assess randomized algorithms in software engineering. In Proceedings of the 33rd international conference on software engineering . 1–10
Andrea Arcuri and Lionel Briand. 2011 · 2011
Earlier work this paper cites.
Improving search-based test suite generation with dynamic symbolic execution. In 2013 ieee 24th international symposium on software reliability engineering (issre) . IEEE, 360–369
Juan Pablo Galeotti, Gordon Fraser, and Andrea Arcuri. 2013 · 2013
Earlier work this paper cites.
Defects4J: A database of existing faults to enable controlled testing studies for Java programs. In Proceedings of the 2014 international symposium on software testing and analysis . 437–440
René Just, Darioush Jalali, and Michael D Ernst. 2014 · 2014
Earlier work this paper cites.
Bidirectional Symbolic Analysis for Effective Branch Testing
Mauro Baluda, Giovanni Denaro, and Mauro Pezzè. 2016 · 2015
Earlier work this paper cites.
Supporting oracle construction via static analysis. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering . 178–189
Junjie Chen, Yanwei Bai, Dan Hao, Lingming Zhang, Lu Zhang, Bing Xie, and Hong Mei. 2016 · 2016
Earlier work this paper cites.
Symbolic execution of complex program driven by machine learning based constraint solving. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering (Singapore, Singapore) (ASE ’16) . Association for Computing Machinery, New York, NY, USA, 554–559
Xin Li, Yongjuan Liang, Hong Qian, Yi-Qi Hu, Lei Bu, Yang Yu, Xin Chen, and Xuandong Li. 2016 · 2016
Earlier work this paper cites.
Many independent objective (MIO) algorithm for test suite generation. In Search Based Software Engineering: 9th International Symposium, SSBSE 2017, Paderborn, Germany, September 9-11, 2017, Proceedings 9 . Springer, 3–17
Andrea Arcuri. 2017 · 2017
Earlier work this paper cites.
Combining symbolic execution and search-based testing for programs with complex heap inputs. In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis (Santa Barbara, CA, USA) (ISSTA 2017) . Association for Computing Machinery, New York, NY, USA, 90–101
Pietro Braione, Giovanni Denaro, Andrea Mattavelli, and Mauro Pezzè. 2017 · 2017
Earlier work this paper cites.
Saying ‘hi!’is not enough: Mining inputs for effective test generation. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 44–49
Luca Della Toffola, Cristian-Alexandru Staicu, and Michael Pradel. 2017 · 2017
Earlier work this paper cites.
Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets
Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Test suite generation with the Many Independent Objective (MIO) algorithm
Andrea Arcuri. 2018 · 2018
Earlier work this paper cites.
Unit Test Case Generation with Transformers
Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, and Neel Sundaresan. 2020 · 2020
Earlier work this paper cites.
Unified Pre-training for Program Understanding and Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 2655–2668
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Graph-based seed object synthesis for search-based unit testing. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 1068–1080
Yun Lin, You Sheng Ong, Jun Sun, Gordon Fraser, and Jin Song Dong. 2021 · 2021
Cited alongside, same era.
Pycg: Practical call graph generation in python. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 1646–1657
Vitalis Salis, Thodoris Sotiropoulos, Panos Louridas, Diomidis Spinellis, and Dimitris Mitropoulos. 2021 · 2021
Cited alongside, same era.
Toga: A neural method for test oracle generation. In Proceedings of the 44th International Conference on Software Engineering . 2130–2141
Elizabeth Dinella, Gabriel Ryan, Todd Mytkowicz, and Shuvendu K Lahiri. 2022 · 2022
Cited alongside, same era.
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Nadia Alshahwan, Mark Harman, Inna Harper, Alexandru Marginean, Shubho Sengupta, and Eddy Wang. 2024b · 2024
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Chatunitest: A framework for llm-based test generation. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering . 572–576
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Pynguin: Automated unit test generation for python. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings . 168–172
Stephan Lukasczyk and Gordon Fraser. 2022 · 2022
Cited alongside, same era.
Generating accurate assert statements for unit test cases using pretrained transformers. In Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test . 54–64
Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, and Neel Sundaresan. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022 · 2022
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A3test: Assertion-augmented automated test case generation
Saranya Alagarsamy, Chakkrit Tantithamthavorn, and Aldeida Aleti. 2023 · 2023
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Contrastive chain-of-thought prompting
Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, and Lidong Bing. 2023 · 2023
Cited alongside, same era.
Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 919–931
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen. 2023 · 2023
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Structured chain-of-thought prompting for code generation
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Yinghao Chen, Zehao Hu, Chen Zhi, Junxiao Han, Shuiguang Deng, and Jianwei Yin. 2024 · 2024
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