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Integrated Development Environments (IDEs) have become central to modern software development, especially with the integration of Artificial Intelligence (AI) to enhance programming efficiency and decision-making.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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Automatically assessing code understandability: How far are we?. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 417–427
Simone Scalabrino, Gabriele Bavota, Christopher Vendome, Mario Linares-Vásquez, Denys Poshyvanyk, and Rocco Oliveto. 2017 · 2017
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A comprehensive model for code readability
Simone Scalabrino, Mario Linares-Vásquez, Rocco Oliveto, and Denys Poshyvanyk. 2018 · 2018
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Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–13
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019 · 2019
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Examining Autocompletion as a Basic Concept for Interaction with Generative AI
Florian Lehmann and Daniel Buschek. 2020 · 2020
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Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2021 · 2021
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SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and its Retrieval
Qing Huang, Dianshu Liao, Zhenchang Xing, Zhiqiang Yuan, Qinghua Lu, Xiwei Xu, and Jiaxing Lu. 2022 · 2022
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Is GitHub Copilot a Substitute for Human Pair-Programming? An Empirical Study. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 319–321
Saki Imai. 2022 · 2022
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Exploring the Learnability of Program Synthesizers by Novice Programmers. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22) . Association for Computing Machinery, New York, NY, USA, Article 64, 15 pages
Dhanya Jayagopal, Justin Lubin, and Sarah E. Chasins. 2022 · 2022
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An Empirical Evaluation of GitHub Copilot’s Code Suggestions. In Proceedings of the 19th International Conference on Mining Software Repositories (Pittsburgh, Pennsylvania) (MSR ’22) . Association for Computing Machinery, New York, NY, USA, 1–5
Nhan Nguyen and Sarah Nadi. 2022 · 2022
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Designing PairBuddy—A Conversational Agent for Pair Programming
Peter Robe and Sandeep Kaur Kuttal. 2022 · 2022
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Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems . ACM, 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L. Glassman. 2022 · 2022
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Better Together? An Evaluation of AI-Supported Code Translation. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22) . Association for Computing Machinery, New York, NY, USA, 369–391
Justin D. Weisz, Michael Muller, Steven I. Ross, Fernando Martinez, Stephanie Houde, Mayank Agarwal, Kartik Talamadupula, and John T. Richards. 2022 · 2022
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Improving Code Autocompletion with Transfer Learning. In Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice (Pittsburgh, Pennsylvania) (ICSE-SEIP ’22) . Association for Computing Machinery, New York, NY, USA, 161–162
Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, and Gareth Ari Aye. 2022 · 2022
Cited alongside, same era.
Albert Ziegler, Eirini Kalliamvakou, X. Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian. 2022 · 2022
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Trust in Generative AI among students: An Exploratory Study
Matin Amoozadeh, David Daniels, Daye Nam, Stella Chen, Michael Hilton, Sruti Srinivasa Ragavan, and Mohammad Amin Alipour. 2023 · 2023
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Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23) . ACM
On the Design of AI-powered Code Assistants for Notebooks
Andrew M. McNutt, Chenglong Wang, Robert A. DeLine, and Steven M. Drucker. 2023 · 2023
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In-IDE Generation-based Information Support with a Large Language Model
Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers. 2023 · 2023
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“It’s Weird That It Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers
James Prather, Brent N. Reeves, Paul Denny, Brett A. Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett Powell, James Finnie-Ansley, and Eddie Antonio Santos. 2023 · 2023
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Human–Computer Interaction
Amon Rapp. 2023 · 2023
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The Programmer’s Assistant: Conversational Interaction with a Large Language Model for Software Development. In Proceedings of the 28th International Conference on Intelligent User Interfaces (Sydney, NSW, Australia) (IUI ’23) . Association for Computing Machinery, New York, NY, USA, 491–514
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Tyler Angert, Miroslav Suzara, Jenny Han, Christopher Pondoc, and Hariharan Subramonyam. 2023 · 2023
Cited alongside, same era.
Is GitHub’s Copilot as Bad as Humans at Introducing Vulnerabilities in Code?
Owura Asare, Meiyappan Nagappan, and N. Asokan. 2023 · 2023
Cited alongside, same era.
Grounded Copilot: How Programmers Interact with Code-Generating Models
Shraddha Barke, Michael B. James, and Nadia Polikarpova. 2023 · 2023
Cited alongside, same era.
Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains
Yu Cheng, Jieshan Chen, Qing Huang, Zhenchang Xing, Xiwei Xu, and Qinghua Lu. 2023 · 2023
Cited alongside, same era.
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Zhen Ming, and Jiang. 2023 · 2023
Cited alongside, same era.
CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming
Felicia Li Feng, Ryan Yen, Yuzhe You, Mingming Fan, Jian Zhao, and Zhicong Lu. 2023 · 2023
Cited alongside, same era.
How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study
Ken Gu, Madeleine Grunde-McLaughlin, Andrew M. McNutt, Jeffrey Heer, and Tim Althoff. 2023 · 2023
Cited alongside, same era.
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
Yuheng Huang, Jiayang Song, Zhijie Wang, Shengming Zhao, Huaming Chen, Felix Juefei-Xu, and Lei Ma. 2023 · 2023
Cited alongside, same era.
Majeed Kazemitabaar, Xinying Hou, Austin Henley, Barbara J. Ericson, David Weintrop, and Tovi Grossman. 2023 · 2023
Cited alongside, same era.
Steven I. Ross, Fernando Martinez, Stephanie Houde, Michael Muller, and Justin D. Weisz. 2023 · 2023
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Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants
Gustavo Sandoval, Hammond Pearce, Teo Nys, Ramesh Karri, Siddharth Garg, and Brendan Dolan-Gavitt. 2023 · 2023
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Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models
Chee Wei Tan, Shangxin Guo, Man Fai Wong, and Ching Nam Hang. 2023 · 2023
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Towards More Effective AI-Assisted Programming: A Systematic Design Exploration to Improve Visual Studio IntelliCode’s User Experience. In Proceedings of the 45th International Conference on Software Engineering: Software Engineering in Practice (Australia) (ICSE-SEIP ’23) . IEEE Press, 185–195
Priyan Vaithilingam, Elena L. Glassman, Peter Groenwegen, Sumit Gulwani, Austin Z. Henley, Rohan Malpani, David Pugh, Arjun Radhakrishna, Gustavo Soares, Joey Wang, and Aaron Yim. 2023 · 2023
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Case Study: Using AI-Assisted Code Generation In Mobile Teams
Mircea-Serban Vasiliniuc and Adrian Groza. 2023 · 2023
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Investigating and Designing for Trust in AI-powered Code Generation Tools
Ruotong Wang, Ruijia Cheng, Denae Ford, and Thomas Zimmermann. 2023 · 2023
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Toward General Design Principles for Generative AI Applications
Justin D. Weisz, Michael Muller, Jessica He, and Stephanie Houde. 2023 · 2023
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Using GitHub Copilot to Solve Simple Programming Problems. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Toronto ON, Canada) (SIGCSE 2023) . Association for Computing Machinery, New York, NY, USA, 172–178
Michel Wermelinger. 2023 · 2023
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On the Concerns of Developers When Using GitHub Copilot
Xiyu Zhou, Peng Liang, Beiqi Zhang, Zengyang Li, Aakash Ahmad, Mojtaba Shahin, and Muhammad Waseem. 2023 · 2023
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