313 citations · 764 across the 53 of their papers we have counts for
58 papers
CTML: A Comprehensive Framework for Continuous Testing in Machine Learning Operations
Bestoun S. Ahmed
The increasing use of machine learning (ML) systems in production has highlighted some important weaknesses in traditional software testing methods. Unlike regular software, ML sys…
End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment
Firas Bayram, Bestoun S. Ahmed, Erik Hallin
This paper introduces a novel end-to-end framework that efficiently integrates data quality assessment with machine learning (ML) model operations in real-time production environme…
Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production
Bestoun S. Ahmed, Tommaso Azzalin, Andreas Kassler +2
We explore a Digital Twin-Based Approach for Smart Manufacturing to improve Sustainability, Efficiency, and Cost-Effectiveness for a steel production plant. Our system is based on…
Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing
Bestoun S. Ahmed, Ludwig Otto Baader, Firas Bayram +2
This paper presents a comprehensive framework for testing and evaluating quality characteristics of Large Language Model (LLM) systems enhanced with Retrieval-Augmented Generation…
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
Firas Bayram, Bestoun S. Ahmed
Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years,…
Adaptive Data Quality Scoring Operations Framework using Drift-Aware Mechanism for Industrial Applications
Firas Bayram, Bestoun S. Ahmed, Erik Hallin
Within data-driven artificial intelligence (AI) systems for industrial applications, ensuring the reliability of the incoming data streams is an integral part of trustworthy decisi…
An Adaptive Metaheuristic Framework for Changing Environments
Bestoun S. Ahmed
The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF)…
Optimizing Service Placement in Edge-to-Cloud AR/VR Systems using a Multi-Objective Genetic Algorithm
Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed +1
Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to…
Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
Manal Rahal, Bestoun S. Ahmed, Jorgen Samuelsson
Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data a…
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks
Firas Bayram, Phil Aupke, Bestoun S. Ahmed +3
Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid s…
A Domain-Region Based Evaluation of ML Performance Robustness to Covariate Shift
Firas Bayram, Bestoun S. Ahmed
Most machine learning methods assume that the input data distribution is the same in the training and testing phases. However, in practice, this stationarity is usually not met and…
DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications
Firas Bayram, Bestoun S. Ahmed, Erik Hallin +1
Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world a…
Automated and Systematic Digital Twins Testing for Industrial Processes
Yunpeng Ma, Khalil Younis, Bestoun S. Ahmed +4
Digital twins (DT) of industrial processes have become increasingly important. They aim to digitally represent the physical world to help evaluate, optimize, and predict physical p…
Quality Assurance in MLOps Setting: An Industrial Perspective
Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin +1
Today, machine learning (ML) is widely used in industry to provide the core functionality of production systems. However, it is practically always used in production systems as par…
A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes
Firas Bayram, Bestoun S. Ahmed, Erik Hallin +1
Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learnin…
Testing of Machine Learning Models with Limited Samples: An Industrial Vacuum Pumping Application
Ayan Chatterjee, Bestoun S. Ahmed, Erik Hallin +1
There is often a scarcity of training data for machine learning (ML) classification and regression models in industrial production, especially for time-consuming or sparsely run ma…
Novel Strategy Generating Variable-length State Machine Test Paths
Vaclav Rechtberger, Miroslav Bures, Bestoun S. Ahmed +1
Finite State Machine is a popular modeling notation for various systems, especially software and electronic. Test paths can be automatically generated from the system model to test…
IoT Anomaly Detection Methods and Applications: A Survey
Ayan Chatterjee, Bestoun S. Ahmed
Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps.…
Prioritized Variable-length Test Cases Generation for Finite State Machines
Vaclav Rechtberger, Miroslav Bures, Bestoun S. Ahmed +3
Model-based Testing (MBT) is an effective approach for testing when parts of a system-under-test have the characteristics of a finite state machine (FSM). Despite various strategie…
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors
Firas Bayram, Bestoun S. Ahmed, Andreas Kassler
The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained…
Overview of Test Coverage Criteria for Test Case Generation from Finite State Machines Modelled as Directed Graphs
Vaclav Rechtberger, Miroslav Bures, Bestoun S. Ahmed
Test Coverage criteria are an essential concept for test engineers when generating the test cases from a System Under Test model. They are routinely used in test case generation fo…
Using Deep Reinforcement Learning for Zero Defect Smart Forging
Yunpeng Ma, Andreas Kassler, Bestoun S. Ahmed +4
Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the enviro…
Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems
Kamal Z. Zamli, Md. Abdul Kader, Saiful Azad +1
This paper discusses a new variant of the Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows multiple clusters ser…
PatrIoT: IoT Automated Interoperability and Integration Testing Framework
Miroslav Bures, Bestoun S. Ahmed, Vaclav Rechtberger +6
With the rapid growth of the contemporary Internet of Things (IoT) market, the established systems raise a number of concerns regarding the reliability and the potential presence o…