Software Engineering Commons^™

Integrating Path Selection For Symbolic Execution And Variable Selection For Constraint Solving, Shunkai Zhu, Jun Sun, Jingyi Wang, Zhenbang Chen, Peng Cheng

Research Collection School Of Computing and Information Systems

Symbolic execution is a powerful technique that can accurately synthesize program inputs for program testing through constraint solving. Applying symbolic execution effectively means that we must solve two searching problems efficiently. One is to search through the many program paths and the other is, given a particular path condition, to search through the numerous variable assignments to identify one satisfying solution. With few exceptions, existing symbolic execution engines treat constraint solvers as black boxes. As a result, the two searches are completely separated, which results in much redundancy (i.e., the same variable assignments may be tried for solving many program …

Decictor: Towards Evaluating The Robustness Of Decision-Making In Autonomous Driving Systems, Mingfei Cheng, Xiaofei Xie, Yuan Zhou, Junjie Wang, Guozhu Meng, Kairui Yang

Research Collection School Of Computing and Information Systems

Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, particularly the ADS's ability to make optimal decisions and produce optimal paths for autonomous vehicles (AVs), is also vital to ensure the intelligence and reduce risks of AVs. Currently, there is little work dedicated to assessing the robustness of ADSs' path-planning decisions (PPDs), i.e., whether an ADS can maintain the optimal PPD after an insignificant change in the environment. The key challenges include the lack of clear oracles for assessing PPD optimality and the difficulty in …

Specgen: Automated Generation Of Formal Program Specifications Via Large Language Models, Lezhi Ma, Shangqing Liu, Yi Li, Xiaofei Xie, Lei Bu

Research Collection School Of Computing and Information Systems

In the software development process, formal program specifications play a crucial role in various stages, including requirement analysis, software testing, and verification. However, manually crafting formal program specifications is rather difficult, making the job time-consuming and labor-intensive. Moreover, it is even more challenging to write specifications that correctly and comprehensively describe the semantics of complex programs. To reduce the burden on software developers, automated specification generation methods have emerged. However, existing methods usually rely on predefined templates or grammar, making them struggle to accurately describe the behavior and functionality of complex real-world programs. To tackle this challenge, we introduce SpecGen, …

Flexfl: Flexible And Effective Fault Localization With Open-Source Large Language Models, Chuyang Xu, Zhongxin Liu, Xiaoxue Ren, Gehao Zhang, Ming Liang, David Lo

Research Collection School Of Computing and Information Systems

Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, …

Ml Playground: Data Modification/Preprocessing And Model Simulation Tool, Marco D. Cerrato

Electronic Theses, Projects, and Dissertations

There is a heavy reliance on programming when it comes to learning machine learning (ML). This often creates barriers for students and newcomers unfamiliar with coding. While the lessons you learn in the classroom provide essential foundational understanding, some technical or practical aspects of ML—such as data preprocessing, feature engineering, and model tuning—are best learned through hands-on interaction. ML Playground was developed to act as a proof-of-concept application to address this gap by offering a browser-based, graphical user interface that lets users engage with core ML workflows without writing code. Designed with educational accessibility in mind, the application allows users …

Towards Visual Inertial Navigation With Fixed Tetrahedral Targets, Joao Leonardo Silva Cotta

Theses and Dissertations

This dissertation presents a robust method for 6DoF position estimation under impaired visual conditions utilizing a minimum 4-point Perspective-n-Point (P4P) solver designed for tetrahedral targets. Using SO(3) × R 3 instead of SE(3), the method uses a Lie group-based formulation to discriminate between rotation and translation, thereby enabling computationally efficient, resource-conscious op- optimization while preserving correct geometric behavior. Designed using the contemporary C++17 library ShomerTarget, the solver is analytically formulated and assessed under pragmatic robotic conditions. Particularly in low-light and high-dynamic environments, experiments on embedded systems, UAVs, and NASA’s Astrobee show that the proposed solver attains enhanced accuracy compared to …

Tailoring Transformer-Based Deep Learning For Code Generation And Translation, Imam Nur Bani Yusuf

Dissertations and Theses Collection (Open Access)

Software is increasingly pervasive in modern society, making the effective translation of human intent into code essential. Novice programmers often struggle with domain-specific code due to limited background knowledge, while experienced developers face challenges in maintaining evolving largescale codebases. Traditional pattern-based approaches address these issues, but such approaches are task-specific and require significant adaptation for different tasks. Transformer-based models offer a more flexible alternative, as the same architecture can be tailored for diverse programming tasks.

This dissertation investigates how Transformer-based models can be customized for various code generation and translation tasks. First, it introduces Transformer-based approaches that assist end-users with …

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 …

Development Of Fuzzy Ontology For Explainable Artificial Intelligence For Decision-Making In Fuzzy Environment, Pavel Kosov

Chemical Technology, Control and Management

In modern artificial intelligence systems, there is an acute need to understand the decision-making logic of "black box" algorithms. Our research proposes an innovative method for increasing the transparency of such systems through the formalization of fuzzy explanatory mechanisms. We have developed an extension of existing ontological approaches by introducing the concept of fuzziness into the structure of explanatory properties, which allows overcoming the fundamental limitations of traditional XAI methods. The proposed formalization is based on the theory of collective mental models and principles of fuzzy logic, providing a more accurate reflection of uncertainty and subjectivity in expert knowledge. Our …

Cascading Effects: Analyzing Project Failure Impact In The Maven Central Ecosystem, Mina Shehata

SPARK Symposium Presentations

Abstract—This study examines failure propagation patterns within the Maven Central ecosystem, a critical software de- pendency repository, through comprehensive analysis of dependency networks using the Goblin framework. Our dual-sampling methodology, investigating both top dependencies and random libraries, revealed two distinct failure propagation patterns that pose significant risks to ecosystem stability. Core infrastructure failures, particularly evident in cases like the AWS SDK family with 429,800 total dependencies, create immediate and widespread disruption, affecting an average of 20,402 dependent projects and propagating through dependency chains averaging 90.80 levels deep.

Our analysis of peripheral projects reveals their significant cascading effects, with higher average …

Simplifying 3d Printing Using Natural Language Processing, Jared E. Rosenberger

Undergraduate Theses

3D printing is a crucial technology with many applications in different fields. To be able to use this technology to its full extent, expertise in computer aided design (CAD) technology and 3D modeling is required. Many people interested in 3D printing do not have this expertise and thus cannot build custom models, and are consequently forced to buy them instead. Natural language processing (NLP) is one tool that can vastly simplify 3D modeling for those lacking CAD experience. Using NLP, someone can simply dictate what they want to be able to print, and a computer can then build a 3D …

Towards Testing, Detecting, And Debloating Insecure Components In Android Applications, Zicheng Zhang

Dissertations and Theses Collection (Open Access)

The Android ecosystem’s openness and extensibility have fueled its dominance in the mobile market, but they also broaden the attack surface of applications by introducing insecure or redundant methods. Vulnerabilities arise from various sources, including insecure API usage, code cloning, and feature bloat, especially from unneeded components introduced during development. To address these challenges, this dissertation presents a systematic, three-phase pipeline that transitions seamlessly from vulnerability discovery to clone-based detection and, ultimately, to dynamic mitigation through runtime debloating. Each phase builds upon the insights and limitations of the previous, collectively forming a practical approach to improving Android app security.

In …

Democratic Training Against Universal Adversarial Perturbations, Bing Sun, Jun Sun, Wei Zhao

Research Collection School Of Computing and Information Systems

Despite their advances and success, real-world deep neural networks are known to be vulnerable to adversarial attacks. Universal adversarial perturbation, an inputagnostic attack, poses a serious threat for them to be deployed in security-sensitive systems. In this case, a single universal adversarial perturbation deceives the model on a range of clean inputs without requiring input-specific optimization, which makes it particularly threatening. In this work, we observe that universal adversarial perturbations usually lead to abnormal entropy spectrum in hidden layers, which suggests that the prediction is dominated by a small number of “feature” in such cases (rather than democratically by many …

The Use Of Call Graphs And Deep Learning To Improve Software Testing, Ziad A. Al-Sharif, Hemanth G. Chintala, Safwan Omari

Engineering, Computing and Mathematical Sciences Faculty Conferences

Software testing is a critical part of software development, it is essential for preventing failures and enhancing software quality attributes. However, the testing process can be costly and time-consuming, often involving a large number of test cases. Over time, the accumulation of redundant and overlapping test cases can complicate and lengthen the testing time. To address these challenges, this paper utilizes graph similarity and deep learning techniques to optimize test suites. It uses call graphs from test cases to identify redundant and similar test cases. A machine learning model is used to calculate and predict the similarity scores between these …

Gamescope, Jake Rankin, Luis Garza, Brain Lujan, Mauricio Rebaza Figueroa

Posters - 2025

Video games have grown exponentially since their debut in the late 20th century. Despite the widespread digitalization and advancements within the gaming community marked by a transition from physical discs to digital downloads and many more major improvements, the lack of an efficient, multipurpose application for reviews remains prevalent. When designing GameScope, we wanted to tackle the key problem of the absence of a multi-platform gaming review system. Gamers currently lack a popular platform to easily find game reviews and get personalized recommendations. Our aim is to create a space where gamers can share their experiences and explore new games …

Mente -Mental Health Tracking App, Vu Han

Posters - 2025

Mental health plays a crucial role in overall well-being, yet many digital tools in this space are either overly complex or lack usercentered design. Mente is a streamlined, web-based application created to support daily mental health engagement through simplicity and ease of use.

•Purpose: To provide a minimal, intuitive platform for users to reflect on their emotional well-being and develop healthier habits over time.

•Core Features:

• Mood tracking with visual trends

• Journaling for personal reflection

• Goal setting and progress tracking

• Health assessment for self-awareness

• Analytics for self-reflection •Design Focus: A clean, distraction-free interface that emphasizes …

Holdfast War Archives, Albert Mendez

Posters - 2025

Holdfast War Archives is a full-stack website designed for the competitive community of the 19th-century multiplayer roleplaying game, Holdfast Nations at War. This project caters to the North American (NA) melee competitive scene, offering tools to enhance player engagement, maintain records, track performance, and facilitate competitive matchmaking.

Ai And Prompt Engineering For Library Discovery Services, James Day

Publications

We have seen the rise of generative artificial intelligence in the form of Large Language Models (LLMs) to provide answers to users’ queries. Services such as ChatGPT, Copilot, and Gemini have quickly become accepted and adopted in the research process. Now library vendors are adding artificial intelligence (AI) to their discovery services to allow for natural language queries to produce generative results. However, the AI model used for discovery services differs from normal LLMs in a significant way that has several positive benefits, but it affects how prompts are written. Library discovery services use a model called Retrieval- Augmented Generation …

Applying Software Engineering Black-Box Methods For Testing Machine Learning Models, Timothy Elvira

Doctoral Dissertations and Master's Theses

This dissertation proposes researching an approach to incorporate and align Software black-box testing methods into Machine Learning (ML) applications, specifically in the context of computer vision models. Typically, testing methods within Software Engineering (SE) encompass a range of test types that assess levels of a software system, such as Unit, Integration, Functional, and System testing [1]. The testing spectrum offers two perspectives on the system: black-box, where the system’s code is hidden, and white-box, where the system's code is exposed for testing. Software Quality pairs testing with requirements, in a many-to-one relationship, to ensure proper validation of the software system. …

A Study Of Preconditions And Postconditions As Design Constraints For Llm Code Generation, Luke Newcomb

Doctoral Dissertations and Master's Theses

Large Language Models (LLMs) have significantly advanced automated code generation, but current methods predominantly rely on natural language descriptions. This approach encounters challenges when handling complex, class-level software generation tasks due to inherent ambiguity and under-specification. Few studies have investigated how more formal software engineering constraints, such as explicit preconditions and postconditions, influence class-level generation tasks. This work addresses this gap through a structured evaluation of six state-of-the-art LLMs generating software implementations from systematically designed class-level specifications. Results demonstrate that incorporating explicit design constraints significantly boosts initial generation accuracy (measured via the pass@k metric), particularly in Python but also in …