Software engineers are increasingly adding semantic search capabilities to applications
using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves …

The increasing usage of machine learning (ML) coupled with the software architectural
challenges of the modern era has resulted in two broad research areas: i) software …

Large Language Models (LLMs) are increasingly integrated into software applications.
Downstream application developers often access LLMs through APIs provided as a service …

In this research paper, we present the results of our collaborative study with Gokind AB on
the integration of artificial intelligence as a service into an existing system. Initially, we …

Agile4MLS - Leveraging Agile Practices for Develo** ML-enabled systems: An Industrial
Experience Page 1 Agile4MLS - Leveraging Agile Practices for Develo** ML-enabled systems …

Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as
finance, medicine, and transportation. The correctness of these ML applications is important …

Machine learning (ML) has revolutionized various industries by enabling the development of
complex models that learn from data and make accurate predictions. However, moving from …

Increased popularity of 'intelligent'web services provides end-users with machine-learnt
functionality at little effort to developers. However, these services require a decision …

The advances in machine learning (ML) have stimulated the integration of their capabilities
into software systems. However, there is a tangible gap between software engineering and …

Machine learning (ML) components are increasingly incorporated into software products for
end-users, but developers face challenges in transitioning from ML prototypes to products …