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AI-based Code Generation: Achievements and Open Problems

Published: 18 June 2024 Publication History

Abstract

Large Language Models (LLMs) have gained significant attention in the software engineering community. Nowadays developers have the possibility to exploit these models through industrial-grade tools providing a handy interface toward LLMs, such as GitHub Copilot. In this talk, I will discuss recent successful applications of LLMs for code generation, showing how they are changing the way in which developers approach coding. Then, I will present open problems in the area of AI-based code generators; this part will cover both issues arising from their usage as well as challenges that the research community working in this area are facing (e.g., related to the empirical evaluation of these tools).

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  1. AI-based Code Generation: Achievements and Open Problems

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    EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering
    June 2024
    728 pages
    ISBN:9798400717017
    DOI:10.1145/3661167
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 June 2024

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    Author Tags

    1. AI for Software Engineering
    2. Generative Models

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    EASE 2024

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    Overall Acceptance Rate 71 of 232 submissions, 31%

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