Software Engineering Commons^™

Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

Research Collection School Of Computing and Information Systems

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …

Ntire 2025 Challenge On Event-Based Image Deblurring: Methods And Results, Lei Sun, Et. Al.

Research Collection School Of Computing and Information Systems

This paper presents an overview of NTIRE 2025, the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-toNoise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for singleimage deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of eventbased image deblurring. We anticipate …

Hvi: A New Color Space For Low-Light Image Enhancement, Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang

Research Collection School Of Computing and Information Systems

Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable inten sity. The former enforces …

Why Does My Transaction Fail? A First Look At Failed Transactions On The Solana Blockchain, Xiaoye Zheng, Zhiyuan Wan, David Lo, Difan Xie, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting …

Less Is More: On The Importance Of Data Quality For Unit Test Generation, Junwei Zhang, Xing Hu, Shan Gao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise on the performance of learning-based test generation models. We first apply the open …

Contested: Consistency-Aided Tested Code Generation With Llm, Jinhao Dong, Jun Sun, Wenjie Zhang, Jinsong Dong, Dan Hao

Research Collection School Of Computing and Information Systems

Recent advancements in large language models (LLMs) have significantly improved code generation, which generates code snippets automatically based on natural language requirements. Despite achieving state-of-the-art performance, LLMs often struggle to generate accurate and reliable code, requiring developers to spend substantial effort debugging and evaluating the generated output. Researchers have proposed leveraging Consistency to select code that passes more tests (inter-consistency) and demonstrates consistent behavior across more counterparts (intra-consistency). However, since the tests themselves are also generated by LLMs, relying on majority voting based on incorrect tests leads to unreliable results. To address this, we propose a lightweight interaction framework that …

A Knowledge Enhanced Large Language Model For Bug Localization, Yue Li, Bohan Liu, Ting Zhang, Zhiqi Wang, David Lo, Lanxin Yang, Jun Lyu, He Zhang

Research Collection School Of Computing and Information Systems

A significant number of bug reports are generated every day as software systems continue to develop. Large Language Models (LLMs) have been used to correlate bug reports with source code to locate bugs automatically. The existing research has shown that LLMs are effective for bug localization and can increase software development efficiency. However, these studies still have two limitations. First, these models fail to capture context information about bug reports and source code. Second, these models are unable to understand the domain-specific expertise inherent to particular projects, such as version information in projects that are composed of alphanumeric characters without …

Sla Evaluation And Composition In Reconfigurable Cloud-Based Services, Michael Iannelli

Dissertations, Theses, and Capstone Projects

Given the business model of offering data and computing services in a cloud setting, a major question arises: How do the services of one cloud provider compare to those of others? With the ubiquitous use of smartphones and tablets, the ability of a cloud provider to support QoS and client mobility becomes paramount. This research proposes a methodology for evaluating service-level agreements (SLAs) between cloud providers and their consumers, with a particular focus on dynamic SLA composition to adapt to changes in the application requirements and the external environment—such as traffic surges, security threats, or evolving business models.

In one …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Helmholtz Cage: Software Development And Implementation For Cubesat Testing, Gustavo A. Cotom Lopez

University Honors Theses

This paper details the successful development and deployment of a Helmholtz cage system, designed to produce precisely controlled magnetic fields for testing and calibration purposes. The core focus was on creating a robust, modular software architecture enabling independent current modulation on each axis, comprehensive serial communication between multiple microcontrollers, and real-time data acquisition from the MR3 magnetometer. All software components, including the serial communication drivers, control algorithms, command line interface, and calibration routines, were developed from the ground up. The system was fully operational upon completion: all hardware components functioned as intended, serial communication with each subsystem was reliable, and …

Hotlangbench, A Tiny Benchmark Suite For Higher-Order Statically Typed Languages, Konstantin Laufer

Computer Science: Faculty Publications and Other Works

This work in progress aims to compare various HOT (higher-order and statically typed, a term coined by Phil Wadler) through reproducible course-grained, wall-time benchmarks. Our overall goals include simplicity, agility, and reproducibility.

There is currently only one benchmark, but it brings out substantial performance differences among the various languages and platforms. It uses function composition and other higher-order constructs to build a pipeline of transformations, along with a brute-force iteration that is computationally expensive for input files specifying large ranges as function domains. We currently include versions in Modern C++, C#, Go, Haskell, Kotlin, Modern (stream-based) Java (24), OCaml, Scala …

Programming A More Efficient Onboarding Process For New Employees, Long H. Pham

Undergraduate Honors Theses

The current onboarding process for new hires in the University of San Diego’s Shiley-Marcos School of Engineering is inefficient. There is no central location where new hires and administrators can track onboarding progress. Both parties have to manage multiple email chains and write their own reminders to keep track of everything. This leads to delays, missing deadlines, and confusion for both parties. A web-based onboarding application has been developed recently to address these issues and streamline the onboarding process for new hires. However, this application contains several accessibility issues and does not follow all of the standards for effective employee …

Evolving Enemy Behavior In Video Games, Hermie H. Adams Iii

Honors Theses

The video game I developed for my senior project lacked complex and engaging enemy artificial intelligence. The standard implementations of AI systems such as finite state machines and behavior trees felt like side-steps rather than innovative solutions. Upon seeing the 'magic' of machine learning in perfecting games such as Snake, Super Mario, and Flappy Bird, I was inspired to seek my answer in the field of evolutionary computation. However, my challenge differed in that the problem space would be defined by dynamic player strategies, making it not well-defined or static. As such, my evaluations are based on enemies exhibiting emergent …

Bridging Cattle Farming And Technology: The Development Of Moomanager, Matthew Hayes

Honors College Theses

Small-scale cattle producers face persistent challenges in adopting digital tools for herd management, often due to barriers such as limited digital literacy, software complexity, and poor alignment with practical workflows. Existing literature highlights the potential benefits of mobile applications in agricultural contexts, yet adoption rates remain low among smaller operations. This thesis investigates how a streamlined, mobile-first application can address these adoption barriers while supporting essential farm management tasks. The study details the design and development of MooManager, a mobile application built with React Native and Supabase and structured around core features such as cattle tracking, beef sales logging, and …

Reverse Engineering Of Binary Programs Using Graph Attention Networks, Sai Nikhila Kanigiri

Master's Theses

Understanding the functionality and behavior of binary code is essential for many software engineering tasks, including malware analysis, vulnerability detection, and program optimization. However, automating this process is challenging due to the complexity of machine code and the significant manual effort required from experienced software engineers. In this paper, we present BinGAT (Reverse Engineering of Binary Programs using Graph Attention Networks), a method for classifying binary programs into algorithmic categories using Graph Attention Neural Networks (GNNs) based on their Control-Flow Graphs (CFGs). Given a binary program, BinGAT extracts its CFG through static analysis and transforms the assembly instructions within each …

Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad

Student Scholar Symposium Abstracts and Posters

Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …

Improving The Reproducibility Of Deep Learning Software: An Initial Investigation Through A Case Study Analysis, Nikita Ravi, Abhinav Goel, James C. Davis, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns about reproducing the results of these deep learning methods. This is significant because reproducibility is the foundation of reliability and validity in software development, particularly in the rapidly evolving domain of deep learning. The difficulty of reproducibility may arise due to several reasons, including having differences from the original execution environment, incompatible software libraries, proprietary data and source code, lack of transparency, and the stochastic nature in some software. A study conducted by …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …