Predictive Test Case Prioritization In Agile Workflows: A Machine Learning Approach With Smote-Enn And Pca,
2026
Multimedia University, Malaysia
Predictive Test Case Prioritization In Agile Workflows: A Machine Learning Approach With Smote-Enn And Pca, Kavinraj Baskaran, Junaidi Abdullah, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai
Journal of Informatics and Web Engineering
Test Case Prioritization (TCP) is an essential part of agile software development, allowing for the identification and execution of critical test cases early in the process to maximize fault detection efficiency. Traditional TCP techniques often depend on static metrics, like code complexity and historical pass/fail rates, that do not conform with the dynamic nature of Agile workflows. To overcome this, the proposed research presents a discriminative machine learning-based predictive prioritization model that incorporates historical defect data, execution history, as well as requirement criticality to improve auspiciousness of TCP. The study assesses a range of machine learning models like Logistic Regression, …
Deep Learning For Skin Cancer Classification Using Data Augmentation And Pre-Trained Cnn Models,
2026
Multimedia University, Malaysia
Deep Learning For Skin Cancer Classification Using Data Augmentation And Pre-Trained Cnn Models, Pa Pa Min, Fang Yong Lim, Jashila Nair Mogan, Astie Darmayantie
Journal of Informatics and Web Engineering
Skin cancer is one of the most common forms of cancer worldwide, and its early detection is critical for effective treatment and improved survival rates. In recent years, advancements in Artificial Intelligence (AI), particularly deep learning, have shown great promise in enhancing the accuracy and efficiency of medical image analysis. This study aims to develop a non-invasive, AI-powered diagnostic system for the early detection and classification of skin cancer using dermoscopic images. The proposed approach leverages deep learning techniques to automatically recognize and differentiate between various types of skin lesions, including both malignant and benign cases. The project focuses on …
Adaptive And Learned Query Optimization: Techniques, Architectures, And Emerging Trends,
2026
Ibn Sina University, Sudan
Adaptive And Learned Query Optimization: Techniques, Architectures, And Emerging Trends, Rehab Mohammed Abdelhalim Mokhtar, Palanichamy Naveen
Journal of Informatics and Web Engineering
As databases continue to grow and complexity there is an urgent need to achieve more efficient and faster query processing. This paper presents a technical review of methods that work towards optimization of various types of queries. It progresses by focusing on basic approaches like rules-based optimization and cost-based optimization. Based on this basis, the paper presents a synthesized reference architecture for adaptive and learned query optimizers as a generic framework derived from prior system. It explains on how the classical optimizer components can be enhanced through runtime monitoring integration and feedback mechanisms. From this perspective, it becomes possible to …
An Intelligent Tourism Recommender System Using Retrieval-Guided Large Language Models,
2026
Multimedia University, Malaysia
An Intelligent Tourism Recommender System Using Retrieval-Guided Large Language Models, Wei-Jun Lim, Nabhan D. Salih, Hairulnizam Mahdin, Ammar Alazab
Journal of Informatics and Web Engineering
Travel planning has become more complex nowadays due to the information overload from numerous online platforms causing travelers to be overwhelmed when selecting destinations, accommodations, and activities that match their preferences and constraints. Traditional tourism recommender systems usually provide static and generic suggestions that fail to match users’ needs and cannot understand natural user language. This research develops and evaluates a Generative AI Chatbot for Tourism Recommendations that use Large Language Models (LLMs) to generate personalized travel itineraries through natural language interaction to solve these limitations. The system can process user natural language inputs including travel origin and destination, trip …
A Framework For Pre-Pages Generator And Visualiser For Journal Publishing Systems: A Case Study,
2026
Multimedia University, Malaysia
A Framework For Pre-Pages Generator And Visualiser For Journal Publishing Systems: A Case Study, Karrthik Nadarajan, Jiaheng Lu
Journal of Informatics and Web Engineering
In the domain of academic publishing, publishers must establish an efficient editorial workflow for managing their publication schedule while producing quality content, as these two processes will ultimately provide a good experience to readers. Many independent publishers utilizing Open Journal Systems (OJS) rely on a manual compilation of "pre-pages" (front matter), including the Issue Cover, the Editorial Board List, and the Table of Contents (TOC), through a manual workflow that is labourintensive and prone to formatting and metadata errors. This study presents a web-based Pre-Pages Generator and Visualiser that automates the pre-publishing process, developed using Python and the Streamlit framework. …
The Application Of Time Series Forecasting Model For Carbon Emissions Prediction In Electrical Grid: A Step Toward Climate Action,
2026
Multimedia University, Malaysia
The Application Of Time Series Forecasting Model For Carbon Emissions Prediction In Electrical Grid: A Step Toward Climate Action, Khim Chen Jason Tan, Keat Soon Tioh, Poh Soon Joseph Ng, Felix Pasila
Journal of Informatics and Web Engineering
This study evaluates the forecasting performance of three models of Autoregressive Integrated Moving Average (ARIMA), Prophet, and Bi-directional Long Short-Term Memory (BiLSTM) in predicting CO₂ emissions from the electricity grid in the United States. Using a real dataset from the California Independent System Operator (CAISO) that records CO₂ emissions at five-minute intervals between 2018 and 2020, the data underwent preprocessing steps including outlier removal, missing value imputation, and feature selection before model training. ARIMA parameters were determined using statistical tests such as ACF, Partial Autocorrelation Function (PACF), and Augmented Dickey Fuller (ADF), while Prophet addressed seasonality and calendar effects. BiLSTM, …
Intelligent Telemedicine Systems: Machine Learning For Remote Diagnosis And Patient Management,
2026
Ravensbourne University London, United Kingdom
Intelligent Telemedicine Systems: Machine Learning For Remote Diagnosis And Patient Management, Oras Baker, Kasthuri Subaramaniam, Asiah Lokman
Journal of Informatics and Web Engineering
Telemedicine has experienced significant evolution in the past 10 years from a communication-driven health service to an intelligent, data-driven environment driven by AI (artificial intelligence), ML (machine learning), IoMT (Internet of Medical Things) and cloud–edge computing. This development has been further advanced through ongoing global health challenges that have seen an ageing population, rising burden of chronic, multi-morbid conditions, healthcare specialist shortage, soaring cost of healthcare and major disruptions like the COVID-19 pandemic. Modern telehealth services are now integrating more machine learning algorithms for remote diagnosis, real-time monitoring the patient, predictive risk stratification and proactive treatment. This paper offers a …
Are We Stuck? Modeling And Detecting Deadlocks In Multi-Autonomous Vehicle Systems,
2026
Singapore Management University
Are We Stuck? Modeling And Detecting Deadlocks In Multi-Autonomous Vehicle Systems, Mingfei Cheng, Xiaofei Xie, Lili Quan, Yuan Zhou
Research Collection School Of Computing and Information Systems
Autonomous driving system (ADS) testing is essential to ensure the safety and reliability of autonomous vehicles (AVs) prior to deployment. As ADSs are increasingly deployed in multi-AV traffic environments, it becomes crucial to assess their cooperative performance, particularly with respect to deadlock, a fundamental liveness issue in concurrent systems that can lead to traffic congestion and prolonged stalling. However, the analysis and testing of ADSs’ cooperative capabilities with respect to deadlock remain largely underexplored. In this work, we present the first systematic study of deadlock in multi-AV systems. We formalize deadlock in autonomous driving using a time-indexed wait-for relation grounded …
Compiling Code Llms Into Lightweight Executables,
2026
Singapore Management University
Compiling Code Llms Into Lightweight Executables, Jieke Shi, Junda He, Zhou Yang, Chengran Yang, Mykhailo Klymenko, Thong (James) Hoang, Sherry (Xiwei) Xu, Zhenchang Xing, David Lo
Research Collection School Of Computing and Information Systems
The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have produced many cloud-based tools for software engineering tasks such as code suggestion. Although effective, cloud deployment raises concerns over privacy, latency, and reliance on network connectivity. Running LLMs locally on personal devices such as laptops would address these issues, because it enables offline use and reduces response time. However, local deployment is challenging, since commodity devices lack high-performance accelerators such as GPUs and are constrained by limited memory and compute capacity, which makes it …
State-Aware Fuzzing Of Javascript Engines With Llm-Guided Instrumentation,
2026
Singapore Management University
State-Aware Fuzzing Of Javascript Engines With Llm-Guided Instrumentation, Wai Kin Wong, Dongwei Xiao, Cheuk Tung Lai, Ping Fan Ke, Shuai Wang
Research Collection School Of Computing and Information Systems
The security of the modern web depends on the correctness of JavaScript (JS) engines, yet these complex systems remain vulnerable to high-impact bugs. A critical limitation of state-of-the-art fuzzers is the coverage plateau: once a fuzzer saturates the control-flow graph, edge coverage loses its ability to guide discovery. Because complex engine behaviors, such as JIT optimization tiers and hidden class transitions, often share identical edge coverage, standard coverage metrics are blind to the distinct internal states required to trigger deep errors. To bridge this gap, we present StateLens, a framework that employs Large Language Models (LLM) to automate the discovery …
Mutation-Based Multi-Agent Test Case Update,
2026
Singapore Management University
Mutation-Based Multi-Agent Test Case Update, Dawei Tian, Jiakun Liu, Yun Peng, Yichen Zhang, Jianlei Chi, Jun Sun, Xiaohong Su
Research Collection School Of Computing and Information Systems
Modern software systems evolve rapidly under CI/CD practices, where tests are critical for quality. However, substantial code changes often render existing test cases obsolete, causing pipeline disruptions, reduced productivity, and compromised quality. Recent automatic test update approaches leverage LLMs to refine test cases via execution feedback and exact-matching context retrieval, prioritizing executability and line coverage but suffering three limitations: (1) neglecting test assertion adequacy, weakening fault detection; (2) relying on coarse line coverage instead of specific uncovered lines/branches; (3) using exact-matching retrieval, which fails for LLM hallucinated queries. To address these, we propose MuMuTestUp, a mutation-guided multi-agent framework with three …
Ddor: Delta Debugging For Explainable Overrefusal Testing And Repair,
2026
Singapore Management University
Ddor: Delta Debugging For Explainable Overrefusal Testing And Repair, Qinyan Zhou, Peixin Zhang, Jun Sun, Haonan Zhang, Dongxia Wang
Research Collection School Of Computing and Information Systems
While safety alignment and guardrails help large language models (LLMs) avoid harmful outputs, they can also induce overrefusal, i.e., unwarranted rejection of benign queries that merely appear risky. We present DDOR (Delta Debugging for OverRefusal), a fully automated and explainable framework for overrefusal testing and repair in a black-box setting, where only model inputs and outputs are accessible and internal safety mechanisms remain opaque. DDOR applies delta debugging to localize minimal refusal-triggering fragments (mRTFs) that provide phrase-level, explainable evidence for why a refusal occurs. Conditioned on these mRTFs, DDOR generates diverse, context-rich prompts and performs multi-oracle validation to filter intrinsically …
Characterizing And Repairing Obsolete Android Gui Tests Under Ui Evolution,
2026
Singapore Management University
Characterizing And Repairing Obsolete Android Gui Tests Under Ui Evolution, Shiwen Song, Yiheng Xiong, Wenbo Guo, Manqi Sun, Jiaolong Kong, Xiaofei Xie
Research Collection School Of Computing and Information Systems
Graphical user interface (GUI) tests are widely used in regression testing of mobile applications (apps) tovalidate app behavior from the user’s perspective. However, frequent app evolution, such as UI redesignsand feature updates, often renders existing GUI tests obsolete, even when underlying functionality remainsunchanged. Automatically repairing such tests is critical for maintaining test suites and reducing substantialmanual effort. Despite the practical importance, there is still a lack of a systematic understanding of thecharacteristics of obsolete GUI tests and a publicly available benchmark to support their study. To fill thisgap, we construct a benchmark comprising 736 obsolete GUI tests collected from 36 …
Probguard: Proactive Runtime Monitoring For Llm Agent Safety Via Probabilistic Prediction,
2026
Singapore Management University
Probguard: Proactive Runtime Monitoring For Llm Agent Safety Via Probabilistic Prediction, Haoyu Wang, Christopher M. Poskitt, Jiali Wei, Jun Sun
Research Collection School Of Computing and Information Systems
Large Language Model (LLM) agents increasingly operate across domains such as robotics, virtual assistants, and web automation. However, their stochastic decision-making introduces safety risks that are difficult to anticipate during execution. Existing runtime monitoring frameworks, such as AgentSpec, primarily rely on reactive safety rules that detect violations only when unsafe behavior is imminent or has already occurred, limiting their ability to handle long-horizon dependencies. We present ProbGuard, a proactive runtime monitoring framework for LLM agents that anticipates safety violations through probabilistic risk prediction. ProbGuard abstracts agent executions into symbolic states and learns a Discrete-Time Markov Chain (DTMC) from execution traces …
Mg-Fuzz: Model-Guided Fuzzing For Unsafe Scenario Discovery In Autonomous Driving Systems,
2026
Singapore Management University
Mg-Fuzz: Model-Guided Fuzzing For Unsafe Scenario Discovery In Autonomous Driving Systems, Yulong Lyu, Ruiqi Hong, Jiawan Wang, Jun Sun, Lei Bu
Research Collection School Of Computing and Information Systems
As autonomous driving systems (ADS) are increasingly deployed in real-world environments, discovering diverse unsafe driving scenarios remains a fundamental yet difficult problem. Existing scenario generation and testing approaches often rely on black-box exploration or externally-observed heuristic feedback, which struggle to effectively guide the search toward high-risk scenarios induced by complex decision-making behaviors. A key difficulty stems from the fact that unsafe behaviors in ADS often arise from internal decision-making logic, which can induce structured and discontinuous responses that are hard to effectively explore using purely black-box guidance. Consequently, current tools tend to repeatedly discover a narrow set of similar unsafe …
A Novel Entropy Based Maintainability Measurement Algorithm For Java Source Code.,
2026
University of Salford
A Novel Entropy Based Maintainability Measurement Algorithm For Java Source Code., Remi M. Yusuf Mr, Md Shadab Mashuk, Julian Bass
Communications of the IIMA
Software metrics play a central role in assessing and managing the quality of software systems providing quantitative insights into attributes such as complexity, reliability, rigidity, modifiability and maintainability. Among these, maintainability is particularly critical, as it directly influences the ease of system evolution, long-term sustainability, and overall cost effectiveness. Despite the widespread use of metric-based maintainability measurement algorithms, capturing a value that reflects the maintainability situation of software source code remains a challenging task, especially in the presence of design deficiencies such as code smells. To measure changes in maintainability, this study experimentaly characterises the relationship between code smells and …
Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems,
2026
Singapore Management University
Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar
Research Collection School of Computing and Information Systems
Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary …
Ecu-Malnett V2,
2026
Edith Cowan University
Ecu-Malnett V2, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone
Research Datasets
ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …
Myoelectric Transradial Prosthesis Motor Control With Temporal Convolutional Neural Network Signal Processing,
2026
Embry-Riddle Aeronautical University
Myoelectric Transradial Prosthesis Motor Control With Temporal Convolutional Neural Network Signal Processing, Carolyn Ascha Richardson, Katherine Clark, Francis Genco, Hope Lea, Tobiah Rosser
Discovery Day - Daytona Beach
Transradial (below-the-elbow) amputees account for more than half of all upper limb amputations. Myoelectric prosthetic arms, which use electromyography (EMG) sensors on the surface of the forearm to convert electrical signals from residual limb muscles and mimic hand movement, are popular options for these amputees. However, these prostheses are limited in residual muscle detection and movement accuracy. The objective of this project is to affordably manufacture an externally-powered transradial prosthesis prototype through EMG time-versus-voltage readings collected with six Delsys Trigno Avanti and eight Thalmic MYO EMG sensor channels placed along the extensor digitorum, extensor carpi radialis, extensor carpi ulnaris, flexor …
From Code To Cube: Interactive Fluid Simulation With Real Time Motion Control,
2026
Embry-Riddle Aeronautical University
From Code To Cube: Interactive Fluid Simulation With Real Time Motion Control, Dominic Ziccardi, Carter Groezinger
Discovery Day - Daytona Beach
This research project explores the use of FluidX3D, an open-source lattice Boltzmann method (LBM) solver, to simulate fluid behavior within a three-dimensional cube container. The system supports both standard water models and rheoscopic fluid visualization, allowing detailed observation of complex flow dynamics in real time. The simulation accurately represents fluid motion, gravity-driven behavior, and rotational response within a bounded cubic domain. The longterm objective is to extend this digital simulation into a physical installation consisting of six synchronized square displays arranged to form a cube. This configuration will create a volumetric illusion of fluid occupying a tangible, handheld structure. An …
