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Large Language Models (LLMs) have demonstrated strong natural language processing and code synthesis capabilities, which has led to their rapid adoption in software engineering applications.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, et al · 1959
Earlier work this paper cites.
Supporting Controlled Experimentation with Testing Techniques: An Infrastructure and its Potential Impact
Hyunsook Do, Sebastian G. Elbaum, and Gregg Rothermel. 2005 · 2005
Earlier work this paper cites.
Mutations: How Close are they to Real Faults?. In 2014 IEEE 25th International Symposium on Software Reliability Engineering . 189–200
Rahul Gopinath, Carlos Jensen, and Alex Groce. 2014 · 2014
Earlier work this paper cites.
Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs. In Proceedings of the 2014 International Symposium on Software Testing and Analysis (ISSTA 2014) . Association for Computing Machinery, New York, NY, USA, 437–440
René Just, Darioush Jalali, and Michael D. Ernst. 2014a · 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 (FSE 2014) . Association for Computing Machinery, New York, NY, USA, 654–665
René Just, Darioush Jalali, Laura Inozemtseva, Michael D. Ernst, Reid Holmes, et al · 2014
Earlier work this paper cites.
FLUCCS: Using Code and Change Metrics to Improve Fault Localization (ISSTA 2017) . Association for Computing Machinery, New York, NY, USA, 273–283
Jeongju Sohn and Shin Yoo. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need. In NIPS
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, et al · 2017
Earlier work this paper cites.
Automatic Software Repair: A Survey
Luca Gazzola, Daniela Micucci, and Leonardo Mariani. 2019 · 2019
Earlier work this paper cites.
BugsJS: a Benchmark of JavaScript Bugs
Péter Gyimesi, Béla Vancsics, Andrea Stocco, Davood Mazinanian, Árpád Beszédes, et al · 2019
Earlier work this paper cites.
IFixR: Bug Report Driven Program Repair. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019) . Association for Computing Machinery, New York, NY, USA, 314–325
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé, Dongsun Kim, Martin Monperrus, et al · 2019
Cited alongside, same era.
TBar: Revisiting Template-Based Automated Program Repair. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2019) . Association for Computing Machinery, New York, NY, USA, 31–42
Kui Liu, Anil Koyuncu, Dongsun Kim, and Tegawendé F. Bissyandé. 2019 · 2019
Cited alongside, same era.
On the Efficiency of Test Suite Based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (ICSE ’20) . Association for Computing Machinery, New York, NY, USA, 615–627
Kui Liu, Shangwen Wang, Anil Koyuncu, Kisub Kim, Tegawendé F. Bissyandé, et al · 2020
Cited alongside, same era.
LangID GitHub Repository
[n. d.] · 2023
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OpenAI official documentation
[n. d.] · 2023
Closest in time.
Automated Repair of Programs from Large Language Models. In Proceedings of the 45th International Conference on Software Engineering (ICSE ’23) . IEEE Press, 1469–1481
Zhiyu Fan, Xiang Gao, Martin Mirchev, Abhik Roychoudhury, and Shin Hwei Tan. 2023 · 2023
Closest in time.
Impact of Code Language Models on Automated Program Repair
Nan Jiang, Kevin Liu, Thibaud Lutellier, and Lin Tan. 2023 · 2023
Closest in time.
Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction. In Proceedings of the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023)
Sungmin Kang, Juyeon Yoon, and Shin Yoo. 2023 · 2023
Closest in time.
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Mozhan Soltani, Annibale Panichella, and Arie van Deursen. 2020 · 2020
Cited alongside, same era.
BugsInPy: a database of existing bugs in Python programs to enable controlled testing and debugging studies
Ratnadira Widyasari, Sheng Qin Sim, Camellia Lok, Haodi Qi, Jack Phan, et al · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, et al · 2021
Cited alongside, same era.
The Art, Science, and Engineering of Fuzzing: A Survey
Valentin J. M. Manès, HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, et al · 2021
Cited alongside, same era.
Training Compute-Optimal Large Language Models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, et al · 2022
Cited alongside, same era.
CodaMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models. In 2023 45th International Conference on Software Engineering (ICSE)
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K. Lahiri, and Siddhartha Sen. 2023 · 2023
Closest in time.
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, et al · 2023
Closest in time.
GPT-4 Technical Report
OpenAI. 2023 · 2023
Closest in time.