Automatic Programming: Large Language Models and Beyond
원문 초록
Automatic programming has seen increasing popularity due to the emergence of tools like GitHub Copilot which rely on Large Language Models (LLMs). At the same time, automatically generated code faces challenges during deployment due to concerns around quality and trust. In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility. These are key issues for organizations while deciding on the usage of automatically generated code. We discuss how advances in software engineering such as program repair and analysis can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future, where programmers may need to switch to different roles to fully utilize the power of automatic programming. Automated repair of automatically generated programs from LLMs, can help produce higher assurance code from LLMs, along with evidence of assurance
함께 살펴볼 논문
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CoSIL: Software Issue Localization via LLM-Driven Code Repository Graph Searching
Less is More: Adaptive Program Repair with Bug Localization and Preference Learning
o3-mini vs DeepSeek-R1: Which One is Safer?
Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair
Planning-Driven Programming: A Large Language Model Programming Workflow
From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging
토론
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