AI-driven Development Is Here: Should You Worry?
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Abstract (arXiv)
AI-Driven Development Environments (AIDEs) Integrate the power of modern AI into IDEs like Visual Studio Code and JetBrains IntelliJ. By leveraging massive language models and the plethora of openly available source code, AIDEs promise to automate many of the obvious, routine tasks in programming. At the same time, AIDEs come with new challenges to think about, such as bias, legal compliance, security vulnerabilities, and their impact on learning programming.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2022.
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Citing papers per year: 2022: 1; 2023: 7; 2024: 5; 2025: 1; 2026: 1.
15 papers.
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- 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
- 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 81
- 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
- 2023How Effective Are Neural Networks for Fixing Security Vulnerabilitiescited by 50
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directionscited by 18
- 2023Can Large Language Models Find And Fix Vulnerable Software?cited by 14
- 2024Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factorscited by 10
- 2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarkscited by 9
- 2024LLASP: Fine-tuning Large Language Models for Answer Set Programmingcited by 8
- 2024Towards Automatic Composition of ASP Programs from Natural Language Specificationscited by 7
- 2024What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric analysiscited by 7
- 2023Study of software developers' experience using the Github Copilot Tool in the software development processcited by 3
- 2026BLAST: Benchmarking LLMs with ASP-based Structured Testing24 Apr 2026
- 2025Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering22 Jun 2025
- 2024What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric analysis29 Nov 2024
- 2024LLASP: Fine-tuning Large Language Models for Answer Set Programming26 Jul 2024
- 2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarks15 Apr 2024
- 2024Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors22 Mar 2024
- 2024Towards Automatic Composition of ASP Programs from Natural Language Specifications7 Mar 2024
- 2023INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers8 Dec 2023
- 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Education1 Oct 2023
- 2023Can Large Language Models Find And Fix Vulnerable Software?20 Aug 2023
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions26 Jul 2023
- 2023How Effective Are Neural Networks for Fixing Security Vulnerabilities29 May 2023
3 more
- 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers5 Apr 2023
- 2023Study of software developers' experience using the Github Copilot Tool in the software development process12 Jan 2023
- 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language27 Oct 2022
- 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
- 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 81
- 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
- 2023How Effective Are Neural Networks for Fixing Security Vulnerabilitiescited by 50
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directionscited by 18
- 2023Can Large Language Models Find And Fix Vulnerable Software?cited by 14
- 2024Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factorscited by 10
- 2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarkscited by 9
- 2024LLASP: Fine-tuning Large Language Models for Answer Set Programmingcited by 8
- 2024Towards Automatic Composition of ASP Programs from Natural Language Specificationscited by 7
- 2024What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric analysiscited by 7
- 2023Study of software developers' experience using the Github Copilot Tool in the software development processcited by 3
12 most cited of 15 All 15 (JSON)
- 2026BLAST: Benchmarking LLMs with ASP-based Structured Testing24 Apr 2026
- 2025Software Reuse in the Generative AI Era: From Cargo Cult Towards AI Native Software Engineering22 Jun 2025
- 2024What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric analysis29 Nov 2024
- 2024LLASP: Fine-tuning Large Language Models for Answer Set Programming26 Jul 2024
- 2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarks15 Apr 2024
- 2024Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors22 Mar 2024
- 2024Towards Automatic Composition of ASP Programs from Natural Language Specifications7 Mar 2024
- 2023INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers8 Dec 2023
- 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Education1 Oct 2023
- 2023Can Large Language Models Find And Fix Vulnerable Software?20 Aug 2023
- 2023Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions26 Jul 2023
- 2023How Effective Are Neural Networks for Fixing Security Vulnerabilities29 May 2023
12 newest of 15 All 15 (JSON)