91 citations · 100 across the 5 of their papers we have counts for
4 papers · 1 filter
Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap
Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin +3
The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered coding assistants, has shown promise in improving developer productivity. H…
Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers
Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin +3
The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhanci…
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
Hao Li, Gopi Krishnan Rajbahadur, Dayi Lin +3
In software engineering, deep learning models are increasingly deployed for critical tasks such as bug detection and code review. However, overfitting remains a challenge that affe…
On the Model Update Strategies for Supervised Learning in AIOps Solutions
Yingzhe Lyu, Heng Li, Zhen Ming +2
AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist sof…