6 citations · 9 across the 6 of their papers we have counts for
6 papers
Compiler.next: A Search-Based Compiler to Power the AI-Native Future of Software Engineering
Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan
The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited…
Predicting post-release defects with knowledge units (KUs) of programming languages: an empirical study
Md Ahasanuzzaman, Gustavo A. Oliva, Ahmed E. Hassan +2
Defect prediction plays a crucial role in software engineering, enabling developers to identify defect-prone code and improve software quality. While extensive research has focused…
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…
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur +9
Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built us…
An empirical study of testing machine learning in the wild
Moses Openja, Foutse Khomh, Armstrong Foundjem +4
Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems…
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…