Combining Contexts from Multiple Sources for Documentation-Specific Code Example Generation
暂无分享,去创建一个
[1] Junaed Younus Khan,et al. Automatic Code Documentation Generation Using GPT-3 , 2022, ASE.
[2] Shuvendu K. Lahiri,et al. Using pre-trained language models to resolve textual and semantic merge conflicts (experience paper) , 2022, ISSTA.
[3] Frank F. Xu,et al. DocPrompting: Generating Code by Retrieving the Docs , 2022, ICLR.
[4] Toufique Ahmed,et al. Few-shot training LLMs for project-specific code-summarization , 2022, ASE.
[5] Immanuel Trummer. CodexDB , 2022, Proceedings of the VLDB Endowment.
[6] Frank F. Xu,et al. A systematic evaluation of large language models of code , 2022, MAPS@PLDI.
[7] Brett A. Becker,et al. The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming , 2022, ACE.
[8] Immanuel Trummer. CodexDB: Synthesizing Code for Query Processing from Natural Language Instructions using GPT-3 Codex , 2022, Proc. VLDB Endow..
[9] S. Savarese,et al. A Conversational Paradigm for Program Synthesis , 2022, ArXiv.
[10] Romain Robbes,et al. Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs , 2021, ArXiv.
[11] Yue Wang,et al. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation , 2021, EMNLP.
[12] Kai-Wei Chang,et al. Retrieval Augmented Code Generation and Summarization , 2021, EMNLP.
[13] Charles Sutton,et al. Program Synthesis with Large Language Models , 2021, ArXiv.
[14] Wojciech Zaremba,et al. Evaluating Large Language Models Trained on Code , 2021, ArXiv.
[15] Namit Katariya,et al. Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization , 2021, NLPMC.
[16] Hieu Tran,et al. CoTexT: Multi-task Learning with Code-Text Transformer , 2021, NLP4PROG.
[17] Kai-Wei Chang,et al. Unified Pre-training for Program Understanding and Generation , 2021, NAACL.
[18] Junaed Younus Khan,et al. Automatic Detection of Five API Documentation Smells: Practitioners’ Perspectives , 2021, 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
[19] Ramesh Karri,et al. Can OpenAI Codex and Other Large Language Models Help Us Fix Security Bugs? , 2021, ArXiv.
[20] Martin Vechev,et al. TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer , 2021, ICML.
[21] Luciano Floridi,et al. GPT-3: Its Nature, Scope, Limits, and Consequences , 2020, Minds and Machines.
[22] Gabriele Bavota,et al. Software Documentation: The Practitioners' Perspective , 2020, 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE).
[23] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[24] Graham Neubig,et al. Incorporating External Knowledge through Pre-training for Natural Language to Code Generation , 2020, ACL.
[25] Ting Liu,et al. CodeBERT: A Pre-Trained Model for Programming and Natural Languages , 2020, FINDINGS.
[26] Omer Levy,et al. Structural Language Models of Code , 2019, ICML.
[27] Mohamed Wiem Mkaouer,et al. Towards Prioritizing Documentation Effort , 2018, IEEE Transactions on Software Engineering.
[28] Graham Neubig,et al. Retrieval-Based Neural Code Generation , 2018, EMNLP.
[29] Dan Klein,et al. Abstract Syntax Networks for Code Generation and Semantic Parsing , 2017, ACL.
[30] Premkumar T. Devanbu,et al. On the naturalness of software , 2016, Perspectives on Data Science for Software Engineering.
[31] Andrew D. Gordon,et al. Bimodal Modelling of Source Code and Natural Language , 2015, ICML.
[32] Martin P. Robillard,et al. How API Documentation Fails , 2015, IEEE Software.
[33] Premkumar T. Devanbu,et al. On the localness of software , 2014, SIGSOFT FSE.
[34] Anh Tuan Nguyen,et al. A statistical semantic language model for source code , 2013, ESEC/FSE 2013.
[35] M. McHugh. Interrater reliability: the kappa statistic , 2012, Biochemia medica.
[36] Martin P. Robillard,et al. A field study of API learning obstacles , 2011, Empirical Software Engineering.
[37] David Lorge Parnas,et al. Precise Documentation: The Key to Better Software , 2010, The Future of Software Engineering.
[38] Martin P. Robillard,et al. What Makes APIs Hard to Learn? Answers from Developers , 2009, IEEE Software.
[39] Nicolas Anquetil,et al. A study of the documentation essential to software maintenance , 2005, SIGDOC '05.
[40] Timothy Lethbridge,et al. The relevance of software documentation, tools and technologies: a survey , 2002, DocEng '02.
[41] Janet Nykaza,et al. What programmers really want: results of a needs assessment for SDK documentation , 2002, SIGDOC '02.
[42] Forrest Shull,et al. Investigating Reading Techniques for Object-Oriented Framework Learning , 2000, IEEE Trans. Software Eng..
[43] Ian Chai,et al. Pedagogical framework documentation: how to document object-oriented frameworks. an empirical study , 1999 .
[44] Hans van der Meij,et al. A critical assessment of the minimalist approach to documentation , 1992, SIGDOC '92.
[45] Mary Beth Rosson,et al. Smalltalk scaffolding: a case study of minimalist instruction , 1990, CHI '90.
[46] John M. Carroll,et al. The Minimal Manual , 1987, SGCH.