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Unit tests represent the most basic level of testing within the software testing lifecycle and are crucial to ensuring software correctness.
A. Van Deursen, L. Moonen, A. Van Den Bergh, and G. Kok, “Refactoring test code,” in Proceedings of the 2nd international conference on extreme programming and flexible processes in software engineering (XP2001) . Citeseer, 2001, pp. 92–95
2001
Earlier work this paper cites.
G. Meszaros, S. M. Smith, and J. Andrea, “The test automation manifesto,” in Conference on extreme programming and agile methods . Springer, 2003, pp. 73–81
2003
Earlier work this paper cites.
C. Csallner and Y. Smaragdakis, “Jcrasher: an automatic robustness tester for java,” Software: Practice and Experience , vol. 34, no. 11, pp. 1025–1050, 2004
2004
Earlier work this paper cites.
P. Tonella, “Evolutionary testing of classes,” ACM SIGSOFT Software Engineering Notes , vol. 29, no. 4, pp. 119–128, 2004
2004
Earlier work this paper cites.
R. Garabík, “Processing xml text with python and elementtree–a practical experience,” Bratislava, L’. Štúr Institute of Linguistics , 2005
2005
Earlier work this paper cites.
C. Pacheco, S. K. Lahiri, M. D. Ernst, and T. Ball, “Feedback-directed random test generation,” in 29th International Conference on Software Engineering (ICSE’07) . IEEE, 2007, pp. 75–84
2007
Earlier work this paper cites.
H. A. Chipman, E. I. George, and R. E. McCulloch, “Bart: Bayesian additive regression trees,” The Annals of Applied Statistics , 2010
2010
Earlier work this paper cites.
G. Fraser and A. Arcuri, “Evosuite: automatic test suite generation for object-oriented software,” in Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering , 2011, pp. 416–419
2011
Earlier work this paper cites.
J. H. Andrews, T. Menzies, and F. C. Li, “Genetic algorithms for randomized unit testing,” Ieee transactions on software engineering , vol. 37, no. 1, pp. 80–94, 2011
2011
Earlier work this paper cites.
A. Sakti, G. Pesant, and Y.-G. Guéhéneuc, “Instance generator and problem representation to improve object oriented code coverage,” IEEE Transactions on Software Engineering , vol. 41, no. 3, pp. 294–313, 2014
2014
Earlier work this paper cites.
L. Ma, C. Artho, C. Zhang, H. Sato, J. Gmeiner, and R. Ramler, “Grt: Program-analysis-guided random testing (t),” in 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2015, pp. 212–223
2015
Earlier work this paper cites.
F. Palomba, D. Di Nucci, A. Panichella, R. Oliveto, and A. De Lucia, “On the diffusion of test smells in automatically generated test code: An empirical study,” in Proceedings of the 9th international workshop on search-based software testing , 2016, pp. 5–14
2016
Earlier work this paper cites.
F. Mariya and D. Barkhas, “A comparative analysis of mutation testing tools for java,” in 2016 IEEE East-West Design & Test Symposium (EWDTS) . IEEE, 2016, pp. 1–3
2016
Cited alongside, same era.
G. Grano, S. Scalabrino, H. C. Gall, and R. Oliveto, “An empirical investigation on the readability of manual and generated test cases,” in Proceedings of the 26th Conference on Program Comprehension , 2018, pp. 348–351
2018
Cited alongside, same era.
A. Peruma, K. Almalki, C. D. Newman, M. W. Mkaouer, A. Ouni, and F. Palomba, “On the distribution of test smells in open source android applications: an exploratory study,” in Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering , 2019, pp. 193–202
2019
Cited alongside, same era.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Cited alongside, same era.
2023
Later among the works it cites.
P. Nie, R. Banerjee, J. J. Li, R. J. Mooney, and M. Gligoric, “Learning deep semantics for test completion,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2111–2123
2023
Later among the works it cites.
Y. Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang, “Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,” in Proceedings of the 32nd ACM SIGSOFT international symposium on software testing and analysis , 2023, pp. 423–435
2023
Later among the works it cites.
N. Nashid, M. Sintaha, and A. Mesbah, “Retrieval-based prompt selection for code-related few-shot learning,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2450–2462
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2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
A. Peruma, K. Almalki, C. D. Newman, M. W. Mkaouer, A. Ouni, and F. Palomba, “Tsdetect: An open source test smells detection tool,” in Proceedings of the 28th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering , 2020, pp. 1650–1654
2020
Cited alongside, same era.
M. Tufano, S. K. Deng, N. Sundaresan, and A. Svyatkovskiy, “Methods2test: A dataset of focal methods mapped to test cases,” in Proceedings of the 19th International Conference on Mining Software Repositories , 2022, pp. 299–303
2022
Cited alongside, same era.
P. Derakhshanfar, X. Devroey, and A. Zaidman, “Basic block coverage for search-based unit testing and crash reproduction,” Empirical Software Engineering , vol. 27, no. 7, p. 192, 2022
2022
Cited alongside, same era.
M. Aniche, Effective Software Testing: A developer’s guide . Simon and Schuster, 2022
2022
Cited alongside, same era.
V. Guilherme and A. Vincenzi, “An initial investigation of chatgpt unit test generation capability,” in Proceedings of the 8th Brazilian Symposium on Systematic and Automated Software Testing , 2023, pp. 15–24
2023
Cited alongside, same era.
M. Schäfer, S. Nadi, A. Eghbali, and F. Tip, “An empirical evaluation of using large language models for automated unit test generation,” IEEE Transactions on Software Engineering , 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Kang, J. Yoon, and S. Yoo, “Large language models are few-shot testers: Exploring llm-based general bug reproduction,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2312–2323
2023
Later among the works it cites.
M. L. Siddiq, J. C. Da Silva Santos, R. H. Tanvir, N. Ulfat, F. Al Rifat, and V. Carvalho Lopes, “Using large language models to generate junit tests: An empirical study,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering , 2024, pp. 313–322
2024
Closest in time.
Y. Tang, Z. Liu, Z. Zhou, and X. Luo, “Chatgpt vs sbst: A comparative assessment of unit test suite generation,” IEEE Transactions on Software Engineering , 2024
2024
Closest in time.
C. S. Xia, M. Paltenghi, J. Le Tian, M. Pradel, and L. Zhang, “Fuzz4all: Universal fuzzing with large language models,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
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2024
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Y. Chen, Z. Hu, C. Zhi, J. Han, S. Deng, and J. Yin, “Chatunitest: A framework for llm-based test generation,” in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering , 2024, pp. 572–576
2024
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