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Much of the cost and effort required during the software testing process is invested in performing test maintenance - the addition, removal, or modification of test cases to keep the test suite in sync with the system-under-test or to otherwise improve its quality.
Language models are few-shot learners,
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., · 1901
Earlier work this paper cites.
An orchestrated survey of methodologies for automated software test case generation,
S. Anand, E. K. Burke, T. Y. Chen, J. Clark, M. B. Cohen, W. Grieskamp, M. Harman, M. J. Harrold, P. McMinn, A. Bertolino, et al., · 2001
Earlier work this paper cites.
A cost model for software maintenance & evolution,
H. M. Sneed, · 2004
Earlier work this paper cites.
G. J. Myers, T. Badgett, T. M. Thomas, C. Sandler, The art of software testing, volume 2, Wiley Online Library, 2004
2004
Earlier work this paper cites.
A case study on regression test suite maintenance in system evolution,
M. Skoglund, P. Runeson, · 2004
Earlier work this paper cites.
M. Kasunic, Designing an effective survey, 2005
2005
Earlier work this paper cites.
Using thematic analysis in psychology,
V. Braun, V. Clarke, · 2006
Earlier work this paper cites.
S. Keele, et al., Guidelines for performing systematic literature reviews in software engineering, 2007
2007
Earlier work this paper cites.
Guidelines for conducting and reporting case study research in software engineering,
P. Runeson, M. Höst, · 2009
Earlier work this paper cites.
Automatically repairing test cases for evolving method declarations,
M. Mirzaaghaei, F. Pastore, M. Pezze, · 2010
Earlier work this paper cites.
Automatic test suite evolution,
M. Mirzaaghaei, · 2011
Earlier work this paper cites.
Understanding myths and realities of test-suite evolution,
L. S. Pinto, S. Sinha, A. Orso, · 2012
Earlier work this paper cites.
Supporting test suite evolution through test case adaptation,
M. Mirzaaghaei, F. Pastore, M. Pezzè, · 2012
Earlier work this paper cites.
The oracle problem in software testing: A survey,
E. T. Barr, M. Harman, P. McMinn, M. Shahbaz, S. Yoo, · 2014
Earlier work this paper cites.
Chronotwigger: A visual analytics tool for understanding source and test co-evolution,
B. Ens, D. Rea, R. Shpaner, H. Hemmati, J. E. Young, P. Irani, · 2014
Earlier work this paper cites.
Automatic test case evolution,
M. Mirzaaghaei, F. Pastore, M. Pezzè, · 2014
Earlier work this paper cites.
Studying fine-grained co-evolution patterns of production and test code,
C. Marsavina, D. Romano, A. Zaidman, · 2014
Earlier work this paper cites.
Maintenance of automated test suites in industry: An empirical study on visual gui testing,
E. Alégroth, R. Feldt, P. Kolström, · 2016
Earlier work this paper cites.
An empirical investigation into the nature of test smells,
M. Tufano, F. Palomba, G. Bavota, M. Di Penta, R. Oliveto, A. De Lucia, D. Poshyvanyk, · 2016
Earlier work this paper cites.
A large-scale study on the usage of testing patterns that address maintainability attributes: patterns for ease of modification, diagnoses, and comprehension,
D. Gonzalez, J. C. Santos, A. Popovich, M. Mirakhorli, M. Nagappan, · 2017
Earlier work this paper cites.
ISO/IEC/IEEE 24765:2017, ISO/IEC/IEEE International Standard - Systems and software engineering, Technical Report ISO/IEC/IEEE 24765:2017, International Organization for Standardization, 2017
2017
Earlier work this paper cites.
Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, · 2017
Earlier work this paper cites.
The co-evolution of test maintenance and code maintenance through the lens of fine-grained semantic changes,
S. Levin, A. Yehudai, · 2017
Earlier work this paper cites.
Survey research in software engineering: Problems and mitigation strategies,
A. N. Ghazi, K. Petersen, S. S. V. R. Reddy, H. Nekkanti, · 2018
Earlier work this paper cites.
Co-evolution analysis of production and test code by learning association rules of changes,
L. Vidács, M. Pinzger, · 2018
Earlier work this paper cites.
A study of automated software testing: Automation tools and frameworks,
M. A. Umar, C. Zhanfang, · 2019
Earlier work this paper cites.
Practitioners’ views on good software testing practices,
P. S. Kochhar, X. Xia, D. Lo, · 2019
Earlier work this paper cites.
A systematic literature review of test breakage prevention and repair techniques,
J. Imtiaz, S. Sherin, M. U. Khan, M. Z. Iqbal, · 2019
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks,
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al., · 2020
Earlier work this paper cites.
Understanding and facilitating the co-evolution of production and test code,
S. Wang, M. Wen, Y. Liu, Y. Wang, R. Wu, · 2021
Earlier work this paper cites.
Pre-trained models: Past, present and future,
X. Han, Z. Zhang, N. Ding, Y. Gu, X. Liu, Y. Huo, J. Qiu, Y. Yao, A. Zhang, L. Zhang, et al., · 2021
Earlier work this paper cites.
On the opportunities and risks of foundation models,
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al., · 2021
Earlier work this paper cites.
Traceability transformed: Generating more accurate links with pre-trained bert models,
J. Lin, Y. Liu, Q. Zeng, M. Jiang, J. Cleland-Huang, · 2021
Cited alongside, same era.
Evaluating large language models trained on code (2021). arXiv:2107.03374
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, W. Zaremba, · 2021
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models,
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al., · 2022
Cited alongside, same era.
Have java production methods co-evolved with test methods properly?: A fine-grained repository-based co-evolution analysis,
T. Kitai, H. Aman, S. Amasaki, T. Yokogawa, M. Kawahara, · 2022
Cited alongside, same era.
Autonomous large language model agents enabling intent-driven mobile gui testing,
J. Yoon, R. Feldt, S. Yoo, · 2023
Later among the works it cites.
Testability refactoring in pull requests: Patterns and trends,
P. Reich, W. Maalei, · 2023
Later among the works it cites.
Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,
C. Lemieux, J. P. Inala, S. K. Lahiri, S. Sen, · 2023
Later among the works it cites.
Fill in the blank: Context-aware automated text input generation for mobile gui testing,
Z. Liu, C. Chen, J. Wang, X. Che, Y. Huang, J. Hu, Q. Wang, · 2023
Later among the works it cites.
Large language models are few-shot testers: Exploring llm-based general bug reproduction,
S. Kang, J. Yoon, S. Yoo, · 2023
Later among the works it cites.
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Cement: On the use of evolutionary coupling between tests and code units. a case study on fault localization,
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Cited alongside, same era.
H. Chase, Langchain, 2022. URL: https://www.langchain.com/
2022
Cited alongside, same era.
React: Synergizing reasoning and acting in language models,
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, Y. Cao, · 2022
Cited alongside, same era.
Patterns of code-to-test co-evolution for automated test suite maintenance,
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Cited alongside, same era.
Enhancing traceability link recovery with unlabeled data,
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Cited alongside, same era.
What is it like to program with artificial intelligence?,
A. Sarkar, A. D. Gordon, C. Negreanu, C. Poelitz, S. S. Ragavan, B. Zorn, · 2022
Cited alongside, same era.
Identify and update test cases when production code changes: A transformer-based approach,
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Cited alongside, same era.
A survey of large language models,
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al., · 2023
Cited alongside, same era.
Llama-reviewer: Advancing code review automation with large language models through parameter-efficient fine-tuning,
J. Lu, L. Yu, X. Li, L. Yang, C. Zuo, · 2023
Later among the works it cites.
Large language models for software engineering: Survey and open problems,
A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, J. M. Zhang, · 2023
Later among the works it cites.
Llm for test script generation and migration: Challenges, capabilities, and opportunities,
S. Yu, C. Fang, Y. Ling, C. Wu, Z. Chen, · 2023
Later among the works it cites.
Large language models: The next frontier for variable discovery within metamorphic testing?,
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Later among the works it cites.
Improving the readability of generated tests using gpt-4 and chatgpt code interpreter,
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Later among the works it cites.
Automated program repair in the era of large pre-trained language models,
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Later among the works it cites.
Code llama: Open foundation models for code,
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al., · 2023
Later among the works it cites.
Towards an understanding of large language models in software engineering tasks,
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Later among the works it cites.
Factors to consider when selecting a large language model: A comparative analysis,
S. Mandvikar, · 2023
Later among the works it cites.
D. Gewirtz, Who owns the code? If ChatGPT’s AI helps write your app, does it still belong to you?, 2023. URL: https://www.zdnet.com/article/who-owns-the-code-if-chatgpts-ai-helps-write-your-app-does-it-still-belong-to-you/
2023
Later among the works it cites.
2023
Later among the works it cites.
Software testing with large language models: Survey, landscape, and vision,
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Towards automatically identifying the co-change of production and test code,
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Large language models are edge-case generators: Crafting unusual programs for fuzzing deep learning libraries,
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Using an llm to help with code understanding,
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