Zobrazeno 1 - 10
of 651
pro vyhledávání: '"HASSAN, AHMED E."'
Traditional code metrics (product and process metrics) have been widely used in defect prediction. However, these metrics have an inherent limitation: they do not reveal system traits that are tied to certain building blocks of a given programming la
Externí odkaz:
http://arxiv.org/abs/2412.02907
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 development of AI judge systems requires a unique engineering life cycle and p
Externí odkaz:
http://arxiv.org/abs/2411.17793
Autor:
Zhang, Haoxiang, Chang, Shi, Leung, Arthur, Thangarajah, Kishanthan, Chen, Boyuan, Lutfiyya, Hanan, Hassan, Ahmed E.
The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A
Externí odkaz:
http://arxiv.org/abs/2411.09580
As foundation models (FMs) play an increasingly prominent role in complex software systems, such as FM-powered agentic software (i.e., Agentware), they introduce significant challenges for developers regarding observability. Unlike traditional softwa
Externí odkaz:
http://arxiv.org/abs/2411.03455
To help MLOps engineers decide which operator to use in which deployment scenario, this study aims to empirically assess the accuracy vs latency trade-off of white-box (training-based) and black-box operators (non-training-based) and their combinatio
Externí odkaz:
http://arxiv.org/abs/2411.00907
The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware--software systems that integrate FMs as core components. While building demonstration-level FMware is relatively straightforward, transitio
Externí odkaz:
http://arxiv.org/abs/2410.20791
Autor:
Dong, Ximing, Wang, Shaowei, Lin, Dayi, Rajbahadur, Gopi Krishnan, Zhou, Boquan, Liu, Shichao, Hassan, Ahmed E.
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 black-box nature hinders its interpretability and effective prompting
Externí odkaz:
http://arxiv.org/abs/2410.13073
Foundation models (FMs) such as large language models (LLMs) have significantly impacted many fields, including software engineering (SE). The interaction between SE and FMs has led to the integration of FMs into SE practices (FM4SE) and the applicat
Externí odkaz:
http://arxiv.org/abs/2410.09012
The rise of AI-assisted software engineering (SE 2.0), powered by Foundation Models (FMs) and FM-powered copilots, has shown promise in improving developer productivity. However, it has also exposed inherent limitations, such as cognitive overload on
Externí odkaz:
http://arxiv.org/abs/2410.06107
The proliferation of open Pre-trained Language Models (PTLMs) on model registry platforms like Hugging Face (HF) presents both opportunities and challenges for companies building products around them. Similar to traditional software dependencies, PTL
Externí odkaz:
http://arxiv.org/abs/2409.10472