Software Engineering Commons^™

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 …

Deepvec: State-Vector Aware Test Case Selection For Enhancing Recurrent Neural Network, Zhonghao Jiang, Meng Yan, Li Huang, Weifeng Sun, Chao Liu, Song Sun, David Lo

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test …

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 …

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 …

Globelly: The Travel App, Diana S. Alvarez

Honors Capstones

My honors capstone project, Globelly: The Travel App, began as a feature-rich Android application designed to simplify travel planning, enhance global exploration, and foster community-driven sharing among travelers. The app was envisioned to integrate real-time suggestions using APIs like Yelp and TripAdvisor, incorporate a badge-based gamification system, and support advanced customization and privacy controls. While not all of these features were implemented in the final version, the project achieved a solid and functional foundation focused on core travel-sharing experiences.

Developed using Java, XML, and the MVC architecture in Android Studio, the completed app allows users to pin locations they’ve …

Tutortech: A Web App For A Smarter And More Efficient Tutoring System, Smarika Pathak

2025 Spring Honors Capstone Projects - Archive

The Computer Science and Engineering (CSE) department faces challenges with managing its tutoring services, especially tracking attendance, booking sessions, and overall management of the tutoring system - all of which severely limits the ability for tutors to connect and engage with students. To help overcome these issues, TutorTech, a web-based application that provides improved management of the tutoring system and supports more engaging learning experiences between students and tutors was designed. Through this project, the aim was to optimize the TutorTech search capabilities - assisting students to find tutors based on skills, while also considering the effect of user interface …

Learning Through Online Participation: From Breakout Rooms To Discord, Makayla Moster

All Dissertations

For software development teams, teamwork is an essential part of their day-to-day lives. However, due to the aftermath of the COVID-19 pandemic, more companies have allowed employees to have more hybrid and remote work options than before. As more companies are adopting hybrid and remote workstyles, we need to ensure that we are preparing the next batch of young software developers to conduct teamwork in remote and hybrid settings. In this dissertation, I address the tools students use for teamwork and how to improve their teamwork inside and outside of the classroom. I present my research on improving student experiences …

Reducing Stigma Around Neurodiversity Through The Use Of Celebratory Technology Ice Breakers In First-Year Undergraduate Classrooms, Briana Craig

Electrical Engineering and Computer Science (MS) Theses

Celebratory technology for Neurodiversity is a new paradigm in the field of human computer interaction; it focuses on reducing stigma surrounding neurodivergent labels and behaviors. Celebratory technology aims to highlight the strengths of neurodiversity rather than fixing socially undesired traits, shifting the responsibility for change from neurodivergent individuals to society's attitudes. Stigma reduction can be accomplished through providing high quality interactions, where anyone can meet and learn about positive traits in others as well as learn of interests' others have in common, thus reframing neurodivergence as inclusion in human diversity rather than a condition to be stigmatized or objectified. This …

“I Can Run At Night!”: Using Augmented Reality To Support Nighttime Guided Running For Low-Vision Runners, Yuki Abe, Keisuke Matsushima, Kotaro Hara, Daisuke Sakamoto, Tetsuo Ono

Research Collection School Of Computing and Information Systems

Dark environment challenges low-vision (LV) individuals to engage in running by following sighted guide—a Caller-style guided running—due to insufficient illumination, because it prevents them from using their residual vision to follow the guide and be aware about their environment. We design, develop, and evaluate RunSight, an augmented reality (AR)-based assistive tool to support LV individuals to run at night. RunSight combines see-through HMD and image processing to enhance one’s visual awareness of the surrounding environment (e.g., potential hazard) and visualize the guide’s position with AR-based visualization. To demonstrate RunSight’s efficacy, we conducted a user study with 8 LV runners. The …