activity
20192024
most citedOptimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing

365 citations · 409 across the 13 of their papers we have counts for

collaborators

14 papers

cs.SE2024★ 2 cited

ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study

Hala Abdelkader, Mohamed Abdelrazek, Scott Barnett +3

Machine learning (ML), especially with the emergence of large language models (LLMs), has significantly transformed various industries. However, the transition from ML model protot…

cs.SE2024★ 9 cited

Seven Failure Points When Engineering a Retrieval Augmented Generation System

Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu +2

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

cs.SE2023★ 11 cited

Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs

Chetan Arora, John Grundy, Mohamed Abdelrazek

Requirements Engineering (RE) is a critical phase in software development including the elicitation, analysis, specification, and validation of software requirements. Despite the i…

cs.SE2023★ 5 cited

Requirements Engineering Framework for Human-centered Artificial Intelligence Software Systems

Khlood Ahmad, Mohamed Abdelrazek, Chetan Arora +3

[Context] Artificial intelligence (AI) components used in building software solutions have substantially increased in recent years. However, many of these solutions focus on techni…

cs.SE2023★ 2 cited

Requirements Elicitation and Modelling of Artificial Intelligence Systems: An Empirical Study

Khlood Ahmad, Mohamed Abdelrazek, Chetan Arora +2

Artificial Intelligence (AI) systems have gained significant traction in the recent past, creating new challenges in requirements engineering (RE) when building AI software systems…

cs.SE2023★ 2 cited

Requirements Practices and Gaps When Engineering Human-Centered Artificial Intelligence Systems

Khlood Ahmad, Mohamed Abdelrazek, Chetan Arora +2

[Context] Engineering Artificial Intelligence (AI) software is a relatively new area with many challenges, unknowns, and limited proven best practices. Big companies such as Google…

cs.CY2021

COVID-19 vs Social Media Apps: Does Privacy Really Matter?

Omar Haggag, Sherif Haggag, John Grundy +1

Many people around the world are worried about using or even downloading COVID-19 contact tracing mobile apps. The main reported concerns are centered around privacy and ethical is…

cs.SE2020★ 9 cited

Requirements of API Documentation: A Case Study into Computer Vision Services

Alex Cummaudo, Rajesh Vasa, John Grundy +1

Using cloud-based computer vision services is gaining traction, where developers access AI-powered components through familiar RESTful APIs, not needing to orchestrate large traini…

cs.SE2020★ 2 cited

RCM: Requirement Capturing Model for Automated Requirements Formalisation

Aya Zaki-Ismail, Mohamed Osama, Mohamed Abdelrazek +2

Most existing automated requirements formalisation techniques require system engineers to (re)write their requirements using a set of predefined requirement templates with a fixed…

cs.SE2020

Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components

Alex Cummaudo, Scott Barnett, Rajesh Vasa +2

Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intellig…

cs.SE2020★ 2 cited

Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow

Alex Cummaudo, Rajesh Vasa, Scott Barnett +2

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services…

cs.SE2019

Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services

Alex Cummaudo, Rajesh Vasa, John Grundy +2

Recent advances in artificial intelligence (AI) and machine learning (ML), such as computer vision, are now available as intelligent services and their accessibility and simplicity…

cs.DC2019

Edge User Allocation with Dynamic Quality of Service

Phu Lai, Qiang He, Guangming Cui +6

In edge computing, edge servers are placed in close proximity to end-users. App vendors can deploy their services on edge servers to reduce network latency experienced by their app…

cs.DC2019★ 365 cited

Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing

Phu Lai, Qiang He, Mohamed Abdelrazek +4

In mobile edge computing, edge servers are geographically distributed around base stations placed near end-users to provide highly accessible and efficient computing capacities and…