Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging,
2026
Singapore Management University
Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng
Research Collection School Of Computing and Information Systems
Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …
Prompting Frameworks For Large Language Models: A Survey,
2026
Singapore Management University
Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang
Research Collection School Of Computing and Information Systems
Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …
Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models,
2026
Singapore Management University
Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models, Jianming Chang, Xin Zhou, Lulu Wang, David Lo, Bixin Li
Research Collection School Of Computing and Information Systems
Automated issue fixing is a critical task in software debugging and has recently garnered significant attention from academia and industry. However, existing fixing techniques predominantly focus on the repair phase, often overlooking the importance of improving the preceding bug localization phase. As a foundational step in issue fixing, bug localization plays a pivotal role in determining the overall effectiveness of the entire process. To enhance the precision of issue fixing by accurately identifying bug locations in large-scale projects, this paper presents BugCerberus, the first hierarchical bug localization framework powered by three customized large language models. First, BugCerberus analyzes intermediate representations …
Match-A-Fit,
2026
St. Mary's University
Match-A-Fit, Brianna Mendoza, Adan Diaz De Leon, Pedro Jacobo, Juan Marco Saca Dada
Posters - 2026
Welcome to Match-a-Fit! Match-a-Fit is an iOS application that allows the user to create a digital closet by uploading images of their clothing items. With AI, the program can generate outfits based on the digital closet, the time, and the occasion. Match-a-Fit’s purpose is designed to help users who struggle to get ready, run out of time, or can’t decide on an outfit, by easily generating outfit options based on the occasion.
Maddenlite,
2026
St. Mary's University
Maddenlite, Sergio Pena
Presentations - 2026
Problem •“What If” scenarios impossible to test accurately •Commercial games rely on arcade physics •Spreadsheets lack visual engagement
Motivation •Passion for football analytics •Desire to simulate cross-era matchups •Apply math models to real-world sports data
Solution •Python based simulation engine using historical play-by-play data •Simulates outcomes based on probability
A.I.R.E.,
2026
St. Mary's University
A.I.R.E., Laurene Robinson
Presentations - 2026
•Cybersecurity analysts rely on reverse engineering to understand suspicious software. •Ghidra can surface decompiled code, but it does not fully explain function purpose, behavioral meaning, or analyst priority. •When symbols are stripped and context is weak, analysts must still reconstruct intent manually from low-level output. •That process is Time-consuming , complex and , operationally costly
Harvest Scanner,
2026
St. Mary's University
Harvest Scanner, Alexander Murphy
Posters - 2026
In current times, people can find themselves at the whims of markets and may be spending more than they realize or want to on regular, everyday goods. New tools can help users keep track of the goods they are paying for. Harvest Scanner was developed to scan and track local grocery prices from stores using their publicly available website information. It was developed in Python using PyQt5 for GUI. The database is stored as an SQL file with Python using SQLite engine. Users will be able to view local grocery prices in a database interface (GUI). There are many features …
A.I.R.E. - Ai-Assisted Reverse Engineering,
2026
St. Mary's University
A.I.R.E. - Ai-Assisted Reverse Engineering, Laurene Robinson
Posters - 2026
Reverse engineering plays a vital role in cybersecurity by helping analysts examine unknown binaries, investigate malware, identify vulnerabilities, and better protect sensitive systems. However, once a program is compiled and stripped, the meaningful names that describe its behavior are lost, leaving behind generic function labels like FUN_00401a30. Analysts must then manually interpret decompiled code, trace call chains, and infer program behavior function by function, which is slow and mentally demanding on large binaries. To address this challenge, this project introduces A.I.R.E., a local Ghidra extension that extracts contextual evidence from stripped functions and uses a locally hosted language model to …
Topshelf,
2026
St. Mary's University
Topshelf, Ayden Jay Soliz
Posters - 2026
With so many great video games releasing each year, it becomes challenging to keep up with the latest. Players find it difficult to maintain an updated list of future games to play, and many existing online trackers have become too complicated to use. TopShelf is designed to be a simple video game backlogging website that will track games for the player. By connecting to an online video game database API, users can add/drop games from their personal list and enable tracking and receive emails for platform releasing. Gamers can leave all the tracking and updates responsibilities to TopShelf
Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing,
2026
Singapore Management University
Penforge: On-The-Fly Expert Agent Construction For Automated Penetration Testing, Huihui Huang, Jieke Shi, Junkai Chen, Ting Zhang, Yikun Li, Chengran Yang, Eng Lieh Ouh, Lwin Khin Shar, David Lo
Research Collection School Of Computing and Information Systems
Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) …
Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution,
2026
Singapore Management University
Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution, Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo
Research Collection School Of Computing and Information Systems
Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, analyzing TEE applications remains challenging due to the high cost and complexity of configuring complete TEE build and runtime environments, as well as the limited observability imposed by hardware isolation. This paper presents SymTEE, a novel large language model (LLM)-assisted symbolic execution framework for detecting missing input validation issues in TEE applications without requiring real TEE setups. SymTEE begins by leveraging Abstract Syntax Tree (AST) analysis to extract TEE code slices that may lack sufficient input validation, and then …
Fallen Light (Video Game),
2026
St. Mary's University
Fallen Light (Video Game), Joshua Do
Presentations - 2026
Problem:
•People misunderstand Lucifer’s deception
•(shown through Temptation of Jesus in Matthew 4) •Lack of engaging ways to teach theological concepts •Background: Lucifer corrupts not through force, but subtle self-elevation and doubt Motivation: •Just initially wanted to make a game of how sin entered the world for fun •Just thought it was a cool idea in general.
Solution:
•Dialogue-driven narrative game •6+ story chapters •10+ regions •Focused on spiritual conflict, deception, and discernment •Uses event-based progression •Presents Lucifer through subtle manipulation, by being a false light by choosing self-pleasure over what is right.
Fisheasy,
2026
St. Mary's University
Fisheasy, Jake Rankin
Presentations - 2026
Problem:
Inexperienced and experienced anglers lack necessary tools to begin fishing
Motivation:
Fishing is a fun hobby everyone should be able to enjoy – the lack of resources makes that difficult
Solution:
An application that integrates learning tutorials with helpful practices tools for both novice and experienced anglers
Foxbuddy,
2026
St. Mary's University
Foxbuddy, Luis Eduardo Garza Jr.
Presentations - 2026
Problem:
•Many people are still unprepared incase of an emergency. (42%-46% are prepared for an emergency)
•Supplies can be scattered, expired, or forgotten. •Reliable guidance is often not easy to access.
Fisheasy,
2026
St. Mary's University
Fisheasy, Jake Ryan Rankin
Posters - 2026
The purpose of FishEasy is to create an all-in-one fishing application that supports both beginner and experienced anglers through education, recommendations, and data tracking. Many new anglers struggle with understanding gear, knots, bait, and locations, while existing tools often limit access through paid features. FishEasy addresses these gaps by providing:
• Educational Tutorials for knots, rigs, and beginner guidance • Smart Recommendations based on location, species, and conditions • Catch Logging & Analytics to track performance • Regulation Awareness for legal fishing practices
Overall, FishEasy aims to make fishing more accessible, efficient, and easy to learn for all users.
Spinlock Game Engine,
2026
St. Mary's University
Spinlock Game Engine, Shane Misley
Posters - 2026
Modern game engines prioritize developer convenience at the cost of performance and transparency. Large frameworks like Unity and Unreal Engine abstract away implementation details, which simplifies development but introduces computational overhead—often 40-50% of CPU and memory usage goes to engine infrastructure rather than the actual game. For developers targeting low-end hardware, older systems, or performance-critical applications, this overhead becomes prohibitive. The Spinlock Engine addresses this problem by adopting a "close-to-the-metal" philosophy, stripping away unnecessary abstraction layers to deliver raw speed and predictable behavior. Built in C++ with SDL3 and Raylib, Spinlock prioritizes memory efficiency, CPU optimization, and developer transparency—allowing you …
Sentra,
2026
St. Mary's University
Sentra, David Aguilar
Posters - 2026
In the fast-paced space of event organization, fostering continuous collaboration among participants is essential. However, organizers often lose valuable time monitoring multiple, disconnected systems once an event is underway. Enter Sentra: an all-in-one Discord bot tailored specifically for weekend events like hackathons. Sentra bridges the gap between participants and organizers by consolidating seamless team matchmaking, robust support ticketing, and automated AI moderation into a single, unified interface.
Synapse,
2026
St. Mary's University
Synapse, Nicolas Diaz, Alexander Murphy, Sonia Cerrillo, Naomi Ramirez, Jesse Kemmer
Posters - 2026
People tend to accumulate a great deal of notes throughout their lives with no coherent way to organize them. Even with the built-in notes app, the notes eventually accumulate until it becomes borderline impossible to find what is needed. Our proposed solution is Synapse, an LLM powered notes app with a tagging system that allows notes to be sorted by topic. The LLM will be able to read the user's notes and recommend tags
Chopchop: The Digital Cookbook,
2026
St. Mary's University
Chopchop: The Digital Cookbook, Dominc Mcdevitt, Shane Misley, Adolfo Duran, Katie Cerda, Kobie Henson
Posters - 2026
Many of today's home chefs still use the same limited methods of saving recipes that have been used for decades, i.e. handwritten notes, disorganized pdfs, screenshots, saved text messages, etc. Not only are these formats hard to keep track of and easily lost, but they also suffer the risk of becoming irrevocably damaged or stained in the cooking process. They are also notoriously hard to edit, which limits a chef's ability to tailor recipes to their taste, their available ingredients, or even just a different serving size. Another major issue with these approaches is the lack of easy sharing. Giving …
Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation,
2026
United Arab Emirates University
Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi
Theses
Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …
