Programming Languages and Compilers Commons^™

Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp

James Madison Undergraduate Research Journal (JMURJ)

No abstract provided.

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 …

Compiling Haskell Into Lean: A Common Abstract Syntax For Haskell And Interactive Theorem Provers, Talitha Holcombe

Electrical Engineering and Computer Science (MS) Theses

In this work, we introduce a program conversion tool, HS-TO-LEAN, that uses GHC's ghc-lib-parser API to translate Haskell programs into Lean code, which is then validated by the Lean compiler. The repo can be found at https://github.com/holcombet/hs-to-lean/tree/main. The result is a successful compilation of a fragment of Haskell into correct and executable Lean code that users can prove theorems about. We conducted a case study using a heap sort algorithm to support our claim that HS-TO-LEAN produces verifiable Lean code. Our approach is inspired by recent advances in formal verification of Haskell programs in Coq, and we currently restrict our …

Evaluation Of Pre-Trained Vision Language Models In Challenging Contexts, Kankan Zhou

Dissertations and Theses Collection (Open Access)

The rapid advancement and proliferation of pre-trained vision-language models (VLMs) have heralded a new era in the realm of artificial intelligence (AI), opening up unprecedented opportunities and challenges alike. This dissertation sets forth on an ambitious and comprehensive journey to critically evaluate the performance and limitations of pre-trained VLMs, particularly in complex and challenging contexts that test the bounds of their capabilities. Our focus is twofold: to rigorously assess the extent of bias embedded in these models, and to meticulously scrutinize their reasoning abilities, highlighting parallels and disparities between machine and human cognition.

We initiate our exploration with a targeted …

Reconfigurable Python Autopilot Software For Rc Aircraft, Kate Doiron

Honors Theses

No abstract provided.

Learning Educational Technology Prototyping With Generative Ai, Justin Olmanson, Azadeh Hassani, Gretchen K. Larsen

Department of Teaching, Learning, and Teacher Education: Faculty Publications

In this study, we use ethnographic methods, grounded theory, and an iterative analytical approach to explore participant experiences and strategies for engaging generative AI in support of both learning how to prototype educational technologies and learning to code. We examine how ChatGPT and Giuseppe (a scaffolded co-coding interface of our own design) influence students’ approaches to prototyping and programming. This study contributes to the field by: identifying specific challenges and affordances of generative AI in prototyping and educational technology development contexts; and offering insights into how educators, students, and learning technology developers can integrate generative AI in formative educational technology …

Comparative Analysis Of Classical And Machine Learning Pathfinding Approaches, Miguel Gapud

Honors Theses

Pathfinding is an essential task for any autonomous robot. Graph-based classical pathfinding algorithms and machine learning approaches have both been used for this end, but they are often not compared against each other. An implementation of end-to-end (E2E) pathfinding using Proximal Policy Optimization (PPO) and an Alexnet architecture is compared against an implementation of Hybrid A*. A digital twin in Unity3D is used as the testing environment with the Clearpath Dingo as the pathfinding robot. In machine learning, the robot is controlled using PPO through ROS-Noetic with a camera as its sensor. Hybrid A* and its controls are implemented directly …

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 …

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu

Research Collection School Of Computing and Information Systems

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers’ intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. …

Tensorjsfuzz: Effective Testing Of Web-Based Deep Learning Frameworks Via Input-Constraint Extraction, Lili Quan, Xiaofei Xie, Qianyu Guo, Lingxiao Jiang, Sen Chen, Junjie Wang, Xiaohong Li

Research Collection School Of Computing and Information Systems

The 2025 ACM Web Conference (WWW '25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its logo, featuring the Sydney Harbour Bridge, symbolizes the core "connecting" function of the Web. Formerly known as the International World Wide Web Conference (WWW), this event originated at CERN in 1994 and has long served as the premier venue for presenting and discussing research, development, standards, and applications related to the Web.The 2025 ACM Web Conference (WWW'25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its …

Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …

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, …

Scenario-Driven And Context-Aware Automated Accessibility Testing For Android Apps, Yuxin Zhang, Sen Chen, Xiaofei Xie, Zibo Liu, Lingling Fan

Research Collection School Of Computing and Information Systems

Mobile accessibility is increasingly important nowadays as it enables people with disabilities to use mobile applications to perform daily tasks. Ensuring mobile accessibility not only benefits those with disabilities but also enhances the user experience for all users, making applications more intuitive and user-friendly. Although numerous tools are available for testing and detecting accessibility issues in Android applications, a large number of false negatives and false positives persist due to limitations in the existing approaches, i.e., low coverage of UI scenarios and lack of consideration of runtime context. To address these problems, in this paper, we propose a scenario-driven exploration …

Hello Again! Llm-Powered Personalized Agent For Long-Term Dialogue, Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-session interactions, neglecting the real-world demands for long-term companionship and personalized interactions with chatbots. Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses. Recent progress in the human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. In response to this potential, we introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent), which incorporates three independently …

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, …

The Role Of Individual Values In Bryant University's Sustainability Efforts, John Boccuzzi Iii

Honors Projects in Data Science

This research examines student, faculty, and staff perspectives on sustainability at Bryant University, with the goal of understanding how individual values align with the university's environmental initiatives. The objective is to assess perceptions of Bryant's current sustainability practices, explore how effectively these efforts are communicated across campus, and identify potential gaps between institutional action and community awareness. To achieve this, the study gathers qualitative data through an open-ended survey and applies sentiment analysis to interpret student attitudes toward sustainability. By analyzing these responses alongside Bryant's sustainability marketing efforts, this research will identify gaps between student engagement and institutional messaging The …

Breaking Down Terminology Of Clojure Error Messages For Beginner Programmers, John Walbran, Jaydon Stanislowski, Tristan Kalvoda

Undergraduate Research Symposium 2025

The Clojure programming language has educational potential for beginner programmers due to its clean, simple syntax and its strong focus on functional programming, an important aspect of CSci education. However, one weakness of Clojure lies in its error messages, which are messages that programmers receive when a program goes wrong. The terminology and shorthands used to convey necessary information for understanding the error are often confusing to novices. The issue is exacerbated by the fact that the error messages are phrased in terms of the underlying programming language – Java – which beginner programmers may typically be unfamiliar with. A …

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

Coding An Assignment Calculator Exclusively With Chatgpt, Andy Tincknell, Heather P. Vandyne, Lisa K. Bell

SACAD: Scholarly Activities

Large Language Models like ChatGPT are influencing higher education and society in broader ways, including the coding and programming of applications and websites (Silva et al., 2024). This poster will profile how Forsyth Library, with no coders on staff, used ChatGPT to program an Assignment Calculator LibGuide without human coding. It details the process, challenges, and outcomes while highlighting AI’s potential to enhance resources for academic success and considers its efficacy and ethical implications.

Binge Buddies, Joshua Uribe

Posters - 2025

Many people struggle to keep track of the shows and movies they’ve watched or plan to watch. Existing streaming platforms often provide limited or cluttered tracking features, making it challenging to stay organized. Binge Buddies addresses this issue by centralizing watchlists and viewing history in one streamlined location. The website is designed to simplify the binge-watching experience, helping users stay on top of their content and discover new shows/movies. Which makes the experience a smoother and more enjoyable experience.

Cyber Safe, Hiram Franco, Laurene Robinson, Joshua Do, Enrique Martinez, Han Vu

Posters - 2025

With the rapid rise of cyber threats, understanding malware behavior is more crucial than ever. Millions of new malware variants emerge annually, compromising personal data, financial information, and entire networks. While no system is entirely immune, cybersecurity education can help mitigate risks. CYBERSAFE is a sandbox malware analyzer designed to enhance malware detection and analysis skills. By running malware samples in a controlled virtual environment, users can observe real-time file modifications, network activity, and system changes. The tool also includes interactive exercises and quizzes to reinforce learning. CYBERSAFE bridges the gap between theory and practice, providing hands-on experience to help …

Frame-Voyager: Learning To Query Frames For Video Large Language Models, Sicheng Yu, Chengkai Jin, Huanyu Wang, Zhenghao Chen, Sheng Jin, Zhongrong Zuo, Xiaolei Xu, Zhenbang Sun, Bingni Zhang, Jiawei Wu, Hao Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it impractical to input entire videos. Existing frame selection approaches, such as uniform frame sampling and text-frame retrieval, fail to account for the information density variations in the videos or the complex instructions in the tasks, leading to sub-optimal performance. In this paper, we propose Frame-Voyager that learns to query informative frame combinations, based on the given textual queries in the task. To train Frame-Voyager, we introduce a new data collection and labeling pipeline, by …

Chatcrs: Incorporating External Knowledge And Goal Guidance For Llm-Based Conversational Recommender Systems, Chuang Li, Yang Deng, Hengchang Hu, Min-Yen Kan, Haizhou Li

Research Collection School Of Computing and Information Systems

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into …

Alphaedit: Null-Space Constrained Knowledge Editing For Language Models, Junfeng Fang, Houcheng Jiang, Kun Wang, Yunshan Ma, Jie Shi, Xiang Wang, Xiangnan He, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Large language models (LLMs) often exhibit hallucinations, producing incorrector outdated knowledge. Hence, model editing methods have emerged to enabletargeted knowledge updates. To achieve this, a prevailing paradigm is the locatingthen-editing approach, which first locates influential parameters and then edits themby introducing a perturbation. While effective, current studies have demonstrated that this perturbation inevitably disrupt the originally preserved knowledge within LLMs, especially in sequential editing scenarios. To address this, we introduce AlphaEdit, a novel solution that projects perturbation onto the null space of the preserved knowledge before applying it to the parameters. We theoretically prove that this projection ensures the output …

A Functional Software Reference Architecture For Llm-Integrated Systems, Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo

Research Collection School Of Computing and Information Systems

The integration of large language models into software systems is transforming capabilities such as natural language understanding, decision-making, and autonomous task execution. However, the absence of a commonly accepted software reference architecture hinders systematic reasoning about their design and quality attributes. This gap makes it challenging to address critical concerns like privacy, security, modularity, and interoperability, which are increasingly important as these systems grow in complexity and societal impact. In this paper, we describe our emerging results for a preliminary functional reference architecture as a conceptual framework to address these challenges and guide the design, evaluation, and evolution of large …

Enhancing Llm-Based Coding Tools Through Native Integration Of Ide-Derived Static Context, Yichen Li, Yun Peng, Yintong Huo, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have achieved remarkable success in code completion, as evidenced by their essential roles in developing code assistant services such as Copilot. Being trained on in-file contexts, current LLMs are quite effective in completing code for single source files. However, it is challenging for them to conduct repository-level code completion for large software projects that require cross-file information. Existing research on LLM-based repository-level code completion identifies and integrates cross-file contexts, but it suffers from low accuracy and limited context length of LLMs. In this paper, we argue that Integrated Development Environments (IDEs) can provide direct, accurate and …

Bootstrapping Language Models With Dpo Implicit Rewards, Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin

Research Collection School Of Computing and Information Systems

Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the process from past work in reinforcement learning from human feedback (RLHF) by bypassing the reward learning stage in RLHF. DPO, after training, provides an implicit reward model. In this work, we make a novel observation that this implicit reward model can by itself be used in a bootstrapping fashion to further align the LLM. Our approach is to use the rewards from a current LLM model to construct a preference dataset, which is then used …