Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have shown remarkable capabilities in processing both natural and programming languages, which have enabled various applications in software engineering, such as requirement engineering, code generation, and software testing.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Two notions of correctness and their relation to testing
Timothy A. Budd and Dana Angluin. 1982 · 1982
Earlier work this paper cites.
Mutation Testing for the New Century
W Eric Wong, J R Horgan, Saul London, and Hira Agrawal. 2000 · 2000
Earlier work this paper cites.
Data mining: practical machine learning tools and techniques with Java implementations
Ian H Witten and Eibe Frank. 2002 · 2002
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.
Is Operator-Based Mutant Selection Superior to Random Mutant Selection?. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 (Cape Town, South Africa) (ICSE ’10) . Association for Computing Machinery, New York, NY, USA, 435–444
Lu Zhang, Shan-Shan Hou, Jun-Jue Hu, Tao Xie, and Hong Mei. 2010 · 2010
Earlier work this paper cites.
Predicting folding free energy changes upon single point mutations
Zhe Zhang, Lin Wang, Yang Gao, Jie Zhang, Maxim Zhenirovskyy, and Emil Alexov. 2012 · 2012
Earlier work this paper cites.
Overcoming the Equivalent Mutant Problem: A Systematic Literature Review and a Comparative Experiment of Second Order Mutation
Lech Madeyski, Wojciech Orzeszyna, Richard Torkar, and Mariusz Józala. 2014 · 2013
Earlier work this paper cites.
Designing Deletion Mutation Operators. In 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation . 11–20
Marcio Eduardo Delamaro, Jeff Offutt, and Paul Ammann. 2014 · 2014
Earlier work this paper cites.
Are mutants a valid substitute for real faults in software testing?. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering . 654–665
René Just, Darioush Jalali, Laura Inozemtseva, Michael D Ernst, Reid Holmes, and Gordon Fraser. 2014 · 2014
Earlier work this paper cites.
How hard does mutation analysis have to be, anyway?. In 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 216–227
Rahul Gopinath, Amin Alipour, Iftekhar Ahmed, Carlos Jensen, and Alex Groce. 2015 · 2015
Earlier work this paper cites.
Trivial compiler equivalence: A large scale empirical study of a simple, fast and effective equivalent mutant detection technique. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering , Vol. 1. IEEE, 936–946
Mike Papadakis, Yue Jia, Mark Harman, and Yves Le Traon. 2015 · 2015
Earlier work this paper cites.
Mutation testing techniques: A comparative study. In 2016 international conference on engineering & MIS (ICEMIS) . IEEE, 1–9
Soukaina Hamimoune and Bouchaib Falah. 2016 · 2016
Earlier work this paper cites.
A Systematic Review of Mutation Testing Tools: A Survey
Pedro Delgado-Pérez, Inmaculada Medina-Bulo, and Antonio García-Domínguez. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
State of mutation testing at google. In Proceedings of the 40th international conference on software engineering: Software engineering in practice . 163–171
Goran Petrović and Marko Ivanković. 2018 · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Cited alongside, same era.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 3539–3549
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Cited alongside, same era.
Mutation Testing Advances: An Analysis and Survey
The robots are coming: Exploring the implications of openai codex on introductory programming. In Proceedings of the 24th Australasian Computing Education Conference . 10–19
James Finnie-Ansley, Paul Denny, Brett A Becker, Andrew Luxton-Reilly, and James Prather. 2022 · 2022
Later among the works it cites.
Can OpenAI’s codex fix bugs? an evaluation on QuixBugs. In Proceedings of the Third International Workshop on Automated Program Repair . 69–75
Julian Aron Prenner, Hlib Babii, and Romain Robbes. 2022 · 2022
Later among the works it cites.
Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen, and Dawn Song. 2022 · 2022
Later among the works it cites.
Probing pretrained models of source code
Sergey Troshin and Nadezhda Chirkova. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mike Papadakis, Marinos Kintis, Jie Zhang, Yue Jia, Yves Le Traon, and Mark Harman. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Code generation as a dual task of code summarization
Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, and Zhi Jin. 2019 · 2019
Cited alongside, same era.
A Primer in BERTology: What we know about how BERT works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020 · 2020
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
Evaluating python, c++, javascript and java programming languages based on software complexity calculator (halstead metrics). In IOP Conference Series: Materials Science and Engineering , Vol. 1076. IOP Publishing, 012046
Sabah A Abdulkareem and Ali J Abboud. 2021 · 2021
Cited alongside, same era.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
Cited alongside, same era.
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.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
Large Language Models for Software Engineering: Survey and Open Problems
Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M Zhang. 2023 · 2023
Later among the works it cites.
Large language models for software engineering: A systematic literature review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2023 · 2023
Later among the works it cites.
Mintlify
Mintlify Inc. 2023a · 2023
Later among the works it cites.
TabNine is an AI-powered code completion tool
TabNine Inc. 2023b · 2023
Later among the works it cites.
The Scope of ChatGPT in Software Engineering: A Thorough Investigation
Wei Ma, Shangqing Liu, Wenhan Wang, Qiang Hu, Ye Liu, Cen Zhang, Liming Nie, and Yang Liu. 2023 · 2023
Later among the works it cites.
Octopack: Instruction tuning code large language models
Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre. 2023 · 2023
Later among the works it cites.
Survey reveals AI’s impact on the developer experience
Inbal Shani and GitHub Staff. 2023 · 2023
Later among the works it cites.
Is ChatGPT the Ultimate Programming Assistant–How far is it?
Haoye Tian, Weiqi Lu, Tsz On Li, Xunzhu Tang, Shing-Chi Cheung, Jacques Klein, and Tegawendé F Bissyandé. 2023 · 2023
Later among the works it cites.
Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
Later among the works it cites.
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. 2023 · 2023
Later among the works it cites.