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The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs).
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Graphcodebert: Pre-training code representations with data flow
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SourceFinder: Finding Malware Source-Code from Publicly Available Repositories in GitHub. In 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020) . USENIX Association, San Sebastian, 149–163
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Evaluating Large Language Models Trained on Code
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Codexglue: A machine learning benchmark dataset for code understanding and generation
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Applying codebert for automated program repair of java simple bugs. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 505–509
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Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 336–347
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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 8696–8708
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Enabling Programming Thinking in Large Language Models Toward Code Generation
Jia Li, Ge Li, Yongming Li, and Zhi Jin. 2023b · 2023
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SkCoder: A Sketch-based Approach for Automatic Code Generation. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . 2124–2135
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StarCoder: may the source be with you!
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Do Pretrained Language Models Indeed Understand Software Engineering Tasks?
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A syntax-guided edit decoder for neural program repair. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 341–353
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021 · 2021
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Code generation tools (almost) for free? a study of few-shot, pre-trained language models on code
Patrick Bareiß, Beatriz Souza, Marcelo d’Amorim, and Michael Pradel. 2022 · 2022
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Toga: A neural method for test oracle generation. In Proceedings of the 44th International Conference on Software Engineering . 2130–2141
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022
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Unixcoder: Unified cross-modal pre-training for code representation
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Competition-level code generation with alphacode
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Mingwei Liu, Tianyong Yang, Yiling Lou, Xueying Du, Ying Wang, and Xin Peng. 2023 · 2023
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Learning performance-improving code edits
Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, and Amir Yazdanbakhsh. 2023 · 2023
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ClarifyGPT: Empowering LLM-based Code Generation with Intention Clarification
Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu, Binquan Zhang, Chenxue Wang, Shichao Liu, and Qing Wang. 2023 · 2023
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Testing LLMs on Code Generation with Varying Levels of Prompt Specificity
Lincoln Murr, Morgan Grainger, and David Gao. 2023 · 2023
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Retrieval-based prompt selection for code-related few-shot learning. In Proceedings of the 45th International Conference on Software Engineering (ICSE’23)
Noor Nashid, Mifta Sintaha, and Ali Mesbah. 2023 · 2023
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SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
Jialing Pan, Adrien Sadé, Jin Kim, Eric Soriano, Guillem Sole, and Sylvain Flamant. 2023b · 2023
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Understanding the Effectiveness of Large Language Models in Code Translation
Rangeet Pan, Ali Reza Ibrahimzada, Rahul Krishna, Divya Sankar, Lambert Pouguem Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand. 2023a · 2023
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From Misuse to Mastery: Enhancing Code Generation with Knowledge-Driven AI Chaining. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 976–987
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S. Mosser S. Arulmohan and M.-J. Meurs. 2023 · 2023
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An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2023 · 2023
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SoTaNa: The Open-Source Software Development Assistant
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Automatic Code Summarization via ChatGPT: How Far Are We?
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The Science of Detecting LLM-Generated Texts
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Codet5+: Open code large language models for code understanding and generation
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ChatCoder: Chat-based Refine Requirement Improves LLMs’ Code Generation
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Using ChatGPT throughout the Software Development Life Cycle by Novice Developers
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Automated program repair in the era of large pre-trained language models. In Proceedings of the 45th International Conference on Software Engineering (ICSE 2023). Association for Computing Machinery
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Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
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A Closer Look at Different Difficulty Levels Code Generation Abilities of ChatGPT. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . 1887–1898
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CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
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Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation
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Llm for test script generation and migration: Challenges, capabilities, and opportunities
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Large Language Models Meet NL2Code: A Survey. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, Toronto, Canada, 7443–7464
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Self-Edit: Fault-Aware Code Editor for Code Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (Eds.). Association for Computational Linguistics, Toronto, Canada, 769–787
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ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers
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Gamma: Revisiting Template-Based Automated Program Repair Via Mask Prediction. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, 535–547
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