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Software testing is a crucial aspect of software development, and the creation of high-quality tests that adhere to best practices is essential for effective maintenance.
Refactoring test code
Arie Van Deursen, Leon Moonen, et al · 2001
Earlier work this paper cites.
Randoop: feedback-directed random testing for java
Carlos Pacheco and Michael D Ernst · 2007
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
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Evosuite: automatic test suite generation for object-oriented software
Gordon Fraser and Andrea Arcuri · 2011
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Are test smells really harmful? an empirical study
Gabriele Bavota, Abdallah Qusef, et al · 2015
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, et al · 2015
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, et al · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, et al · 2016
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An empirical investigation into the nature of test smells
Michele Tufano, Fabio Palomba, et al · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, et al · 2017
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On the relation of test smells to software code quality
Davide Spadini, Fabio Palomba, et al · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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PySE: Automatic Worst-Case Test Generation by Reinforcement Learning
Jinkyu Koo, Charitha Saumya, et al · 2019
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Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications
Yavuz Koroglu and Alper Sen · 2019
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On the distribution of test smells in open source Android applications: an exploratory study
Anthony Peruma, Khalid Almalki, et al · 2019
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Investigating developer perception on test smells using better code hub
Martin Schvarcbacher, D. Spadini, et al · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, et al · 2020
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tsDetect: an open source test smells detection tool
Anthony Peruma, Khalid Almalki, Christian D Newman, Mohamed Wiem Mkaouer, Ali Ouni, and Fabio Palomba · 2020
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Quickly generating diverse valid test inputs with reinforcement learning
Sameer Reddy, Caroline Lemieux, et al · 2020
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Investigating Severity Thresholds for Test Smells
Davide Spadini, Martin Schvarcbacher, et al · 2020
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, et al · 2020
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Fine-tuning language models from human preferences
Daniel M. Ziegler, Nisan Stiennon, et al · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, et al · 2021
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Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, et al · 2023
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Transformer-based vulnerability detection in code at edittime: Zero-shot, few-shot, or fine-tuning?
Aaron Chan, Anant Kharkar, et al · 2023
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Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, et al · 2023
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Effective test generation using pre-trained large language models and mutation testing
Arghavan Moradi Dakhel, Amin Nikanjam, et al · 2023
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CodaMosa: Escaping coverage plateaus in test generation with pre-trained large language models
Caroline Lemieux, Jeevana Priya Inala, et al · 2023
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Unit testing C# with MSTest and .NET
Microsoft · 2023
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Automation of software test data generation using genetic algorithm and reinforcement learning
Mehdi Esnaashari and Amir Hossein Damia · 2021
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GitHub Copilot, 2021
GitHub · 2021
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Learning to complete code with sketches
Daya Guo, Alexey Svyatkovskiy, et al · 2021
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Automated Test Generation for Hardware Trojan Detection using Reinforcement Learning
Zhixin Pan and Prabhat Mishra · 2021
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Unit test case generation with transformers and focal context
Michele Tufano, Dawn Drain, et al · 2021
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Toga: A neural method for test oracle generation
Elizabeth Dinella, Gabriel Ryan, et al · 2022
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CodexLeaks: Privacy leaks from code generation language models in GitHub copilot
Liang Niu, Shujaat Mirza, et al · 2023
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Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, et al · 2023
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Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, et al · 2023
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An empirical evaluation of using large language models for automated unit test generation
Max Schäfer, Sarah Nadi, et al · 2023
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Exploring the effectiveness of large language models in generating unit tests
Mohammed Latif Siddiq, Joanna C. S. Santos, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, et al · 2023
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Chatunitest: a chatgpt-based automated unit test generation tool
Zhuokui Xie, Yinghao Chen, et al · 2023
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Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Shunyu Yao, Dian Yu, et al · 2023
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No more manual tests? evaluating and improving chatgpt for unit test generation
Zhiqiang Yuan, Yiling Lou, et al · 2023
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URL https://doi.org/10.6084/m9.figshare.25983166
Data package, 2024 · 2024
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