Software Engineering Commons^™

Gencode: A Generic Data Augmentation Framework For Boosting Deep Learning-Based Code Understanding, Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCode follows a generation-and-selection paradigm to prepare useful training code data. Specifically, it employs code augmentation techniques to generate new code candidates first and then identifies important ones as the training data by influence scores. To evaluate the effectiveness of GenCode, we …

Llm-Based Stock Sentiment And Market Intelligence Platform, Joshua Thrower, Andrew Pinkerton, Ian Duggan, Wyatt Lester

ATU Scholars Symposium

Financial markets increasingly react to social media discourse, yet investors lack tools to translate this unstructured commentary into measurable indicators. Platforms such as YouTube host extensive discussions about publicly traded equities, but extracting reliable sentiment trends from high-volume, noisy comment streams remains technically challenging. This project develops a stock sentiment and market intelligence platform that transforms YouTube comment data into aggregated sentiment indicators aligned to specific equities. Comments are mapped to equities using ticker specific keyword identification combined with contextual filtering to reduce false associations from ambiguous or off-topic mentions. The system assigns numerical sentiment scores to individual comments and …

Towards Physics-Informed Neural Networks For Simulating Multiphase Geothermal Convection​*, Daniel C. Patton, Andrew Harrison Eno

Campus Research Month

Water and steam flow through porous rock, transferring heat via conduction and buoyancy-driven convection caused by density differences. Traditional numerical methods (finite-volume/finite-element) model this well but can become memory-intensive and unstable for long, high-detail simulations. This work demonstrates a Physics-Informed Neural Network (PINN) using a finite-difference approach within the NVIDIA PhysicsNeMo framework to simulate magma chambers in 2D. Tested on the Rio Pisco pluton in Peru, results are compared with the USGS HYDROTHERM model. PINNs learn from physical laws, offering accurate, flexible solutions with less data and development effort.

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.

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

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 …

Patchgpt: Multi-Agent Patch Backporting Without Model Fine-Tuning, Ye Liu, Ruidong Han, Chengyan Ma, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Patch backporting is crucial and prevalent in the maintenance of modern open-source software such as Linux kernels and forked repositories. However, porting patches across program versions remains a challenging problem due to the complexity of synergizing diverse patches with divergent program versions. In this paper, we propose PatchGPT, an agentic patch backporting framework for fine-grained patch generation. PatchGPT encompasses three agents: Miner for decomposing a sequence of atomic change steps as the original patch plan, Adapter for adapting the patch plan, and Executor for executing the adapted patch plan according to predefined change semantics. We conduct experiments on the PPatHF’s …

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) …

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, 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

Match-A-Fit, Adan Diaz De Leon, Juan Marco Saca Dada, Brianna Mendoza, Arsalan Kataneh, Theophile Nsabimana, Pedro Jacobo

Presentations - 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.

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, 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 …

Understanding Codebase Like A Professional! Human-Ai Collaboration For Code Comprehension, Jie Gao, Yue Xue, Xiaofei Xie, Junming Cao, Soemin Thant, Erika Lee, Bowen Xu

Research Collection School Of Computing and Information Systems

Understanding an unfamiliar codebase is an essential task for developers in various scenarios, such as during the onboarding process. Especially when the codebase is large and time is limited, achieving a decent level of comprehension remains challenging for both experienced and novice developers, even with the assistance of large language models (LLMs). Existing studies have shown that LLMs often fail to support users in understanding code structures or to provide user-centered, adaptive, and dynamic assistance in real-world settings.To address this, we propose learning from the perspective of a unique role, code auditors, whose work often requires them to quickly familiarize …

Autologger: A Multi-Agent Framework For The End-To-End Automated Logging, Renyi Zhong, Yintong Huo, Wenwei Gu, Yichen Li, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose AutoLogger, a novel hybrid framework that addresses the complete the end-to-end logging pipeline. AutoLogger first employs a fine-tuned classifier, the Judger, to accurately determine if a method requires new logging statements. If logging is needed, a multi-agent system is activated. The system includes specialized agents: a Locator dedicated to determining where to log, and a Generator focused on …

Context Engineering For Ai Agents In Open-Source Software, Seyedmoein Mohsenimofidi, Matthias Galster, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

GenAI-based coding assistants have disrupted software development. The next generation of these tools is agent-based, operating with more autonomy and potentially without human oversight. Like human developers, AI agents require contextual information to develop solutions that are in line with the standards, policies, and workflows of the software projects they operate in. Vendors of popular agentic tools (e.g., Claude Code) recommend maintaining version-controlled Markdown files that describe aspects such as the project structure, code style, or building and testing. The content of these files is then automatically added to each prompt. Recently, AGENTS.md has emerged as a potential standard that …

On Autopilot? An Empirical Study Of Human-Ai Teaming And Review Practices In Open Source, Haoyu Gao, Peerachai Banyongrakkul, Hao Guan, Mansooreh Zahedi, Christoph Treude

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of “AI as a teammate” in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers’ interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

How Agile Became The Design Philosophy Of Ai Fishbowl Under Real-World Constraints, Jad Saad

University Honors Theses

This capstone review examines the development of AI Fishbowl, a public-facing, interactive artificial intelligence system, as a case study in how Agile methods evolve from a project management tool into a design philosophy under real-world constraints. Although the project adopted an Agile workflow early on through a Kanban-style task management approach, the initial system design and architecture were still shaped by a largely plan-first mindset. This created a mismatch between flexible process and rigid design assumptions, which became increasingly apparent as the team moved from high-level architecture into implementation.

A critical turning point occurred when early architectural plans proved difficult …

Identifying And Mitigating Api Misuse In Large Language Models, Terry Yue Zhuo, Junda He, Jiamou Sun, Zhenchang Xing, David Lo, John Grundy, Xiaoning Du

Research Collection School Of Computing and Information Systems

API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development. While LLMs demonstrate impressive code generation capabilities, their interactions with complex library APIs are often error-prone, potentially leading to software failures and vulnerabilities. In this paper, we conduct a large-scale study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java, using three representative LLMs (StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot). Based on extensive manual annotation of 3,209 method-level and 3,492 parameter-level misuses, we identify and categorize four recurring misuse types by building on …

Large Language Model Communication And Data Transfer Across A Simulated Telephone Line, Jared Reyes

Dissertations and Theses

This thesis presents a proof-of-concept communication system in which two local large language model endpoints communicate across a simulated analog telephone line using legacy USB modems. The project combines modem voice mode, modem data mode, local speech processing, and structured machine messaging into a single staged session. During the voice phase, one endpoint places a call, the other answers, speech generated by a locally hosted LLaMA-family model is synthesized with Piper, transmitted through the modem voice path, captured on the remote side, and transcribed with Faster-Whisper to drive the next response. After the voice exchange, the system transitions to a …

Algorithmic Trading In Idiosyncratic-Payoff Markets: A Multi-Agent System For On-Chain Prediction Contracts, Saif Aldeen A.K. Agha

CMC Senior Theses

This thesis documents the design, deployment, and forward-test evaluation of an evolutionary multi-agent algorithmic trading system on Polymarket, the largest decentralized prediction market. The system pairs a locally-hosted 72-billion-parameter language model with a gradient-boosted statistical filter and an evolutionary selection mechanism that maintains a population of approximately 500 autonomous trading agents. Each agent generates a probability estimate for an event, compares it to the prevailing market price, and trades the resulting disagreement.

The central empirical exercise estimates a panel regression of trade-level profit on the absolute disagreement between the agent's probability estimate and the market price, controlling for agent identity, …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

A Web-Based Wizard-Of-Oz Platform For Collaborative And Reproducible Human-Robot Interaction Research, Sean O'Connor

Honors Theses

The Wizard-of-Oz (WoZ) technique is widely used in Human-Robot Interaction (HRI) research, but two persistent problems limit its effectiveness: existing tools impose technical barriers that exclude non-engineering domain experts (the Accessibility Problem), and the fragmented landscape of robot-specific implementations makes interaction scripts difficult to port across platforms (the Reproducibility Problem- concerning execution consistency and portability, not third-party replication). Through a literature review, I identified three design principles to address both: a hierarchical specification model, an event-driven execution model, and a plugin architecture that decouples experiment logic from robot-specific implementations. I realized these principles in HRIStudio, an open-source, web-based platform providing …

An Empirical Framework For Evaluating Semantic Preservation Using Hugging Face, Nan Jia, Anita Raja, Raffi Khatchadourian

Publications and Research

As machine learning (ML) becomes an integral part of high-autonomy systems, it is critical to ensure the trustworthiness of learning-enabled software systems (LESS). Yet, the nondeterministic and run-time-defined semantics of ML complicate traditional software refactoring. We define semantic preservation in LESS as the property that optimizations of intelligent components do not alter the system's overall functional behavior. This paper introduces an empirical framework to evaluate semantic preservation in LESS by mining model evolution data from HuggingFace. We extract commit histories, $\textit{Model Cards}$, and performance metrics from a large number of models. To establish baselines, we conducted case studies in three …

Error-Driven Density Control For Compact Gaussian Splatting Under Sparse Supervision, Abdelrhman Elrawy

Theses and Dissertations (Comprehensive)

This thesis studies efficiency and stability challenges in Gaussian-splatting-based reconstruction under sparse supervision. In few-shot novel view synthesis, standard 3D Gaussian Splatting (3DGS) can overfit the limited training views and grow an unnecessarily large number of primitives due to limitations in its Adaptive Density Control (ADC) mechanism. This thesis introduces an error-driven reformulation of ADC that triggers densification using opacity gradients as a lightweight proxy for rendering error, and shows that such aggressive densification must be paired with delayed and conservative pruning to prevent destructive create--destroy cycles. When combined with depth-based geometric regularization, the resulting framework produces substantially more compact …

Geovig And Purevig: Geometry-Aware Architectures For Efficient Computer Vision, Omar Ismail

Theses and Dissertations (Comprehensive)

Deploying deep learning models for medical image analysis on mobile devices requires a balance between inference latency, memory footprint, and delineating anatomical boundaries with high accuracy. While Convolutional Neural Networks (CNNs) and mobile Vision Transformers (ViTs) offer efficiency, they often struggle to model the irregular, non-local geometric structures inherent in biological tissues without incurring prohibitive computational costs. In this thesis, we introduce GeoViG (Geometric Vision Graph), an architecture that bridges the gap between efficient grid-based processing and explicit Geometric Deep Learning. GeoViG introduces a novel transition from high-resolution pixel grids to low-resolution dynamic graphs via a SpreadEdgePool operator, a geometry-aware …