Singapore Management University
St. Mary's University
Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories,
2026
Singapore Management University
Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories, Georgia M. Kapitsaki, Maria Papoutsoglou, Christoph Treude, Ioanna Theophilou
Research Collection School Of Computing and Information Systems
Context: Privacy legislation has impacted the way software systems are developed, prompting practitioners to update their implementations. Specifically, the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have forced the community to focus on users’ data privacy. Objectives: Relying on the vast amount of data on developer issues available in GitHub repositories, our aim is to gather empirical evidence on the issues developers of Open Source Software discuss to comply with privacy legislation. Method: We examined such discussions by mining and analyzing 32,820 issues from GitHub repositories. We partially analyzed the dataset automatically to identify …
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 …
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 …
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. …
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 …
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 …
Generalized Cloud-Based Compressible Aerodynamics Calculator And Simulation Web App,
2026
Embry-Riddle Aeronautical University
Generalized Cloud-Based Compressible Aerodynamics Calculator And Simulation Web App, Massimo Mansueto, Liam Griesacker, Andres Torres-Figueroa
Discovery Day - Daytona Beach
The Generalized Cloud-Based Compressible Aerodynamics Calculator and Simulation Web App focuses on the development of a tool to support the analysis, visualization, and teaching of compressible aerodynamics. In the case of most undergraduate aerospace engineering courses, students rely on static equations, charts, and manual calculations, which can make it difficult to conceptualize complex flow phenomena such as shock waves, expansion fans, and nozzle flow. The purpose of this project is to create an accessible platform that integrates a compressible flow calculator, nozzle sizing tool, and interactive simulations into a single educational resource. The application is implemented using modern web development …
A Hybrid Llm-Srgm Framework For Ai-Enabled Reliability Assessment In Safety-Critical Software Systems,
2026
Embry-Riddle Aeronautical University
A Hybrid Llm-Srgm Framework For Ai-Enabled Reliability Assessment In Safety-Critical Software Systems, Caleb Stone, Shrenik Jadhav
Discovery Day - Daytona Beach
Ensuring the reliability of software intensive and safety critical systems is a persistent challenge across aerospace, defense, transportation, and other mis- sion focused domains. Traditional software relia- bility growth models (SRGM) provide useful quanti- tative insight into defect discovery trends, but they rely mostly only on numerical failure data and do not use the rich contextual information contained in test logs, anomaly reports, and engineering notes. This paper presents a hybrid framework that com- bines semantic features extracted by a large lan- guage model (LLM) with a non-homogeneous Pois- son process (NHPP) based software reliability growth model. The LLM analyzes …
Rockin’ Rover On The Rainbow Road,
2026
Palm Beach Atlantic University
Rockin’ Rover On The Rainbow Road, Michael Kolta, Lawrence Burgee, Ying Yuan
Transformations
This paper presents a progressive series of age-appropriate lesson plans for grades K-12 that all use the same interdisciplinary activity to educate students about Science, Technology, Engineering, Art, and Mathematics (STEAM) simultaneously. Technology from Texas Instruments (TI) was employed including a TI Nspire graphing calculator that can run Python programs, a TI Innovator Hub, and a TI Rover. The TI Rover is a small, robotic car that has sensors and is controlled by the calculator via the Hub hardware interface. A Python program was developed that uses the color sensor in the Rover to detect the color on colored paper …
Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead,
2026
Singapore Management University
Llm-As-A-Judge For Software Engineering: Literature Review, Vision, And The Road Ahead, Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude, Jiamou Sun, Zhenchang Xing, Xiaoning Du, David Lo
Research Collection School Of Computing and Information Systems
The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks from code generation to program repair, producing a massive volume of software artifacts. This surge in automated creation has exposed a critical bottleneck: the lack of scalable and reliable methods to evaluate the quality of these outputs. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality references and struggle to capture nuanced aspects of software quality, such as readability and usefulness. In response, the LLM-as-a-Judge paradigm, which employs LLMs for automated evaluation, has emerged. This approach leverages …
Leveraging Deep Learning Recurrence And Attention Mechanisms For Flood Forecasting And Assessment,
2026
Clemson University
Leveraging Deep Learning Recurrence And Attention Mechanisms For Flood Forecasting And Assessment, Elnaz Heidari
All Dissertations
Predicting how much water will flow in rivers and streams is important for managing floods, water supply, and the environment. Traditionally, government agencies have used complex models, such as the National Water Model (NWM), which simulate how much water moves through landscapes using physical laws and real-world data. However, recent advances in Artificial Intelligence (AI) have enabled new ways to make these predictions. This research explored whether AI-based models could predict river discharge more accurately. These AI models learn patterns from past data instead of relying only on physical rules. To find out how well they work, the AI models …
Lessons From The Club Homeschool Capstone: Testing, Data Discipline, And The Computer Science Curriculum,
2026
Portland State University
Lessons From The Club Homeschool Capstone: Testing, Data Discipline, And The Computer Science Curriculum, Shane Brown
University Honors Theses
This thesis looks at the CLUB Homeschool Capstone project to argue that Portland State University's Computer Science curriculum should introduce testing and data quality discipline earlier and more intentionally than it does now. As team lead of a seven-person team, I coordinated sprint planning, communicated with the sponsor, and developed custom Discourse plugins that enhanced an existing forum platform instead of creating a separate application database, as requested by the sponsor. The project's requirements document called for a formal testing plan, but our team lacked the practical experience to implement one. This gap became evident through my internships as a …
Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States,
2026
Kennesaw State University
Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury
Dissertations
The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …
Configuring Agentic Ai Coding Tools: An Exploratory Study,
2026
Singapore Management University
Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes
Research Collection School Of Computing and Information Systems
Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in …
Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review,
2026
Singapore Management University
Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar
Research Collection School Of Computing and Information Systems
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …
Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models,
2026
Singapore Management University
Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo
Research Collection School Of Computing and Information Systems
Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …
Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap,
2026
Singapore Management University
Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap, Christoph Treude
Research Collection School Of Computing and Information Systems
AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools.In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development …
Operationalizing Ethics For Ai Agents: How Developers Encode Values Into Repository Context Files,
2026
Singapore Management University
Operationalizing Ethics For Ai Agents: How Developers Encode Values Into Repository Context Files, Christoph Treude, Sebastian Baltes, Marc Cheong
Research Collection School Of Computing and Information Systems
As AI coding agents become embedded in software development workflows, developers are beginning to operationalize ethical principles by encoding behavioral rules into repository-level context files for AI agents, such as AGENTS.md files. Rather than examining the ethics of AI agents in the abstract, this vision paper investigates how ethics and values are already being translated for AI agents into actionable instructions that shape agent behavior. Through a preliminary investigation, we find that developers are already embedding guidance related to fairness, accessibility, sustainability, tone, and privacy. These artifacts function as a developer-authored governance layer, translating abstract principles into situated, natural-language directives …
