Application of Large Language Models to Software Engineering Tasks: Opportunities, Risks, and Implications

Применение больших языковых моделей к задачам разработки программного обеспечения: возможности, риски и последствия
İpek Özkaya
2023-04-26

AI-generated codeLarge Language Modelsmisinformation and hallucinationsrisks and implications of LLMssoftware engineering tasks
Has the day we all have been waiting for really arrived? Have advances in deep learning and machine learning (ML) finally reached a turning point and have started to produce “accurate enough” assistants to help us in a variety of tasks, including software development? Are large language models (LLM) going to turn us all into better writers, artists, translators, programmers, health-care workers, not to mention software engineers? Or are we at a risky turning point where we will not be able to separate artificial intelligence (AI)-generated content from user-created ones, drowning in misinformation and perfect sounding yet fake and incorrect information and AI-generated faulty programs?
1
AI-generated faulty programs are a concrete concern, implying risks specific to software engineering when relying on LLM outputs.
2
LLMs have potential to improve productivity across many professions, including writing, art, translation, programming, and healthcare.
3
Recent advances in deep learning/ML and large language models (LLMs) may have reached a turning point enabling assistants that are 'accurate enough' to help with software development tasks.
4
There is a significant risk that AI-generated content will be indistinguishable from human-created content, leading to misinformation and convincingly incorrect information.

Application of large language models (LLMs) to software engineering tasks

Opportunities, risks, and implications of using LLMs for software engineering, including their effectiveness as assistants, potential to improve developer productivity, and risks of misinformation and faulty AI-generated code

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2023-04-26
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İpek Özkaya
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