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most citedRethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers

6 citations · 19 across the 21 of their papers we have counts for

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11 papers · 1 filter

cs.SE2024

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…

cs.LG2024

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…

cs.AI2024

Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents

Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski +2

Large language models (LLMs) are increasingly integrated into autonomous systems, giving rise to a new class of software known as Agentware, where LLM-powered agents perform comple…

cs.CL2024★ 1 cited

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…

cs.SE2024

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…

cs.SE2024

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…

cs.SE2024★ 1 cited

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…

cs.DB2024

SimClone: Detecting Tabular Data Clones using Value Similarity

Xu Yang, Gopi Krishnan Rajbahadur, Dayi Lin +3

Data clones are defined as multiple copies of the same data among datasets. Presence of data clones between datasets can cause issues such as difficulties in managing data assets a…

cs.SE2024★ 6 cited

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…

cs.CL2024

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…

cs.SE2024★ 2 cited

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…