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Despite extensive usage in high-performance, low-level systems programming applications, C is susceptible to vulnerabilities due to manual memory management and unsafe pointer operations.
A Deductive Approach to Program Synthesis
Zohar Manna and Richard Waldinger. 1980 · 1980
Earlier work this paper cites.
The concept of dynamic analysis
Thomas Ball. 1999 · 1999
Earlier work this paper cites.
LLVM: A compilation framework for lifelong program analysis & transformation. In International symposium on code generation and optimization, 2004. CGO 2004. IEEE, 75–86
Chris Lattner and Vikram Adve. 2004 · 2004
Earlier work this paper cites.
A Survey of Reverse Engineering and Program Comprehension
Michael L. Nelson. 2005 · 2005
Earlier work this paper cites.
Realizing quality improvement through test driven development: results and experiences of four industrial teams
Nachiappan Nagappan, E Michael Maximilien, Thirumalesh Bhat, and Laurie Williams. 2008 · 2008
Earlier work this paper cites.
Recursive program synthesis. In Computer Aided Verification: 25th International Conference, CAV 2013, Saint Petersburg, Russia, July 13-19, 2013. Proceedings 25 . Springer, 934–950
Aws Albarghouthi, Sumit Gulwani, and Zachary Kincaid. 2013 · 2013
Earlier work this paper cites.
Syntax-guided synthesis. In 2013 Formal Methods in Computer-Aided Design . 1–8
Rajeev Alur, Rastislav Bodik, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa. 2013 · 2013
Earlier work this paper cites.
SoK: Eternal War in Memory. In 2013 IEEE Symposium on Security and Privacy . 48–62
László Szekeres, Mathias Payer, Tao Wei, and Dawn Song. 2013 · 2013
Earlier work this paper cites.
immunant/c2rust
Immunant Inc. 2020 · 2020
Earlier work this paper cites.
Unsupervised Translation of Programming Languages. In NeurIPS
Baptiste Rozière, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020 · 2020
Earlier work this paper cites.
Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
Earlier work this paper cites.
Fault-aware neural code rankers
Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andres Codas, Mark Encarnación, Shuvendu Lahiri, Madanlal Musuvathi, and Jianfeng Gao. 2022 · 2022
Earlier work this paper cites.
Jigsaw: Large Language Models meet Program Synthesis. In ICSE 2022 (Pittsburgh, Pennsylvania)
Naman Jain, Skanda Vaidyanath, Arun Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram Rajamani, and Rahul Sharma. [n.d.] · 2022
Earlier work this paper cites.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
Earlier work this paper cites.
Leveraging Automated Unit Tests for Unsupervised Code Translation. In ICLR . OpenReview.net
Baptiste Rozière, Jie Zhang, François Charton, Mark Harman, Gabriel Synnaeve, and Guillaume Lample. 2022 · 2022
Earlier work this paper cites.
Parsel : Algorithmic Reasoning with Language Models by Composing Decompositions
Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D Goodman, and Nick Haber. 2022 · 2022
Earlier work this paper cites.
Test-Driven Development Benefits Beyond Design Quality: Flow State and Developer Experience. In 2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) . IEEE, 106–111
Pedro Calais and Lissa Franzini. 2023 · 2023
Earlier work this paper cites.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023 · 2023
Earlier work this paper cites.
Emitting Safer Rust with C2Rust
Immunant Inc. 2024 · 2023
Earlier work this paper cites.
Llm-assisted code cleaning for training accurate code generators
Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E Gonzalez, Koushik Sen, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Attention, Compilation, and Solver-based Symbolic Analysis are All You Need
Prithwish Jana, Piyush Jha, Haoyang Ju, Gautham Kishore, Aryan Mahajan, and Vijay Ganesh. 2023 · 2023
Cited alongside, same era.
On the Evaluation of Neural Code Translation: Taxonomy and Benchmark. In Automated Software Engineering (ASE) . IEEE, 1529–1541
Mingsheng Jiao, Tingrui Yu, Xuan Li, Guanjie Qiu, Xiaodong Gu, and Beijun Shen. 2023 · 2023
Cited alongside, same era.
Lever: Learning to verify language-to-code generation with execution. In International Conference on Machine Learning . PMLR, 26106–26128
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin. 2023 · 2023
Cited alongside, same era.
A Survey on Large Language Models for Code Generation
Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim. 2024 · 2024
Closest in time.
SWE-bench: Can Language Models Resolve Real-world Github Issues?. In The Twelfth International Conference on Learning Representations
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R Narasimhan. 2024 · 2024
Closest in time.
jwerle/url
jwerle. 2024 · 2024
Closest in time.
An Empirical Study of Rust-for-Linux: The Success, Dissatisfaction, and Compromise. In 2024 USENIX Annual Technical Conference (USENIX ATC 24) . USENIX Association, Santa Clara, CA, 425–443
Hongyu Li, Liwei Guo, Yexuan Yang, Shangguang Wang, and Mengwei Xu. 2024a · 2024
Closest in time.
Translating C To Rust: Lessons from a User Study
Ruishi Li, Bo Wang, Tianyu Li, Prateek Saxena, and Ashish Kundu. 2024b · 2024
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Is Self-Repair a Silver Bullet for Code Generation?. In The Twelfth International Conference on Learning Representations
Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2023 · 2023
Cited alongside, same era.
Code translation with Compiler Representations
Marc Szafraniec, Baptiste Roziere, Hugh Leather Francois Charton, Patrick Labatut, and Gabriel Synnaeve. 2023 · 2023
Cited alongside, same era.
Explain-then-translate: an analysis on improving program translation with self-generated explanations. In Findings of the Association for Computational Linguistics: EMNLP 2023 . Association for Computational Linguistics, 1741–1788
Zilu Tang, Mayank Agarwal, Alexander Shypula, Bailin Wang, Derry Wijaya, Jie Chen, and Yoon Kim. 2023 · 2023
Cited alongside, same era.
Ownership guided C to Rust translation
HanLiang Zhang, C. David, Y. Yu, and M. Wang. 2023a · 2023
Cited alongside, same era.
Codeplan: Repository-level coding using llms and planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B Ashok, and Shashank Shet. 2024 · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. 2024 · 2024
Cited alongside, same era.
rust-out-your-c
carol-10-cents. 2024 · 2024
Cited alongside, same era.
Knowledge transfer from high-resource to low-resource programming languages for code llms
Federico Cassano, John Gouwar, Francesca Lucchetti, Claire Schlesinger, Anders Freeman, Carolyn Jane Anderson, Molly Q Feldman, Michael Greenberg, Abhinav Jangda, and Arjun Guha. 2024 · 2024
Cited alongside, same era.
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Large language model-based agents for software engineering: A survey
Junwei Liu, Kaixin Wang, Yixuan Chen, Xin Peng, Zhenpeng Chen, Lingming Zhang, and Yiling Lou. 2024 · 2024
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Can Language Models Solve Olympiad Programming?
Quan Shi, Michael Tang, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
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Reflexion: Language agents with verbal reinforcement learning
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Context-aware Code Segmentation for C-to-Rust Translation using Large Language Models
Momoko Shiraishi and Takahiro Shinagawa. 2024 · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
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StructCoder: Structure-Aware Transformer for Code Generation
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Planning In Natural Language Improves LLM Search For Code Generation
Evan Wang, Federico Cassano, Catherine Wu, Yunfeng Bai, Will Song, Vaskar Nath, Ziwen Han, Sean Hendryx, Summer Yue, and Hugh Zhang. 2024a · 2024
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Agentless: Demystifying LLM-based Software Engineering Agents
Chunqiu Steven Xia, Yinlin Deng, Soren Dunn, and Lingming Zhang. 2024 · 2024
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Swe-agent: Agent-computer interfaces enable automated software engineering
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Scalable, Validated Code Translation of Entire Projects using Large Language Models
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Scaling LLM Inference with Optimized Sample Compute Allocation
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