1 citations · 3 across the 19 of their papers we have counts for
7 papers · 1 filter
Engineering AI Judge Systems
Jiahuei Lin, Dayi Lin, Sky Zhang +1
AI judge systems are designed to automatically evaluate Foundation Model-powered software (i.e., FMware). Due to the intrinsic dynamic and stochastic nature of FMware, the developm…
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan
To balance the quality and inference cost of a Foundation Model (FM, such as large language models (LLMs)) powered software, people often opt to train a routing model that routes r…
PromptExp: Multi-granularity Prompt Explanation of Large Language Models
Ximing Dong, Shaowei Wang, Dayi Lin +4
Large Language Models excel in tasks like natural language understanding and text generation. Prompt engineering plays a critical role in leveraging LLM effectively. However, LLMs…
From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
Gopi Krishnan Rajbahadur, Gustavo A. Oliva, Dayi Lin +2
The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While buil…
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…
Data Quality Antipatterns for Software Analytics
Aaditya Bhatia, Dayi Lin, Gopi Krishnan Rajbahadur +2
Background: Data quality is vital in software analytics, particularly for machine learning (ML) applications like software defect prediction (SDP). Despite the widespread use of ML…
A Framework for Real-time Safeguarding the Text Generation of Large Language Model
Ximing Dong, Dayi Lin, Shaowei Wang +1
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks but also pose ethical and societal risks due to their propensity to generate harmfu…