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One of the critical phases in software development is software testing.
Language models are few-shot learners
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Test generation for higher-order functions in dynamic languages
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Large language models are few-shot testers: Exploring LLM-based general bug reproduction
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Mutation testing advances: an analysis and survey, in: Advances in Computers. Elsevier. volume 112, pp. 275–378
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Evaluating large language models trained on code
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Learning Test-Driven Development
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Nessie: automatically testing javascript apis with asynchronous callbacks, in: Proceedings of the 44th International Conference on Software Engineering, pp. 1494–1505
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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, pp. 54–64
Tufano, M., Drain, D., Svyatkovskiy, A., Sundaresan, N., 2022 · 2022
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Github copilot labs
Alvarado, I., Gazit, I., Wattenberger, A., 2023 · 2023
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The replication package
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Refactory
Hu, Y., Ahmed, U.Z., Mechtaev, S., Leong, B., Roychoudhury, A., 2023 · 2023
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Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models, in: Accepted by 45th International Conference on Software Engineering (ICSE)
Lemieux, C., Inala, J.P., Lahiri, S.K., Sen, S., 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G., 2023 · 2023
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An empirical study of automated unit test generation for python
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Github Copilot AI pair programmer: Asset or liability?
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Retrieval-based prompt selection for code-related few-shot learning
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Adaptive test generation using a large language model
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Exploring the effectiveness of large language models in generating unit tests
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Llama 2: Open foundation and fine-tuned chat models
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