92 citations · 266 across the 32 of their papers we have counts for
32 papers
Trust in AI among Middle Eastern CS Students: Investigating Students' Trust and Usage Patterns Across Saudi Arabia, Kuwait and Jordan
Saleh Alkhamees, Ali Alfageeh, Bader Alkhazi +2
Background and Context: Artificial intelligence (AI) tools have been reshaping computing and computer science education. Trust in AI is a determining factor in the adoption of thes…
Capturing the Effects of Quantization on Trojans in Code LLMs
Aftab Hussain, Sadegh AlMahdi Kazemi Zarkouei, Md Rafiqul Islam Rabin +3
Large language models of code exhibit high capability in performing diverse software engineering tasks, such as code translation, defect detection, text-to-code generation, and cod…
From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions
Ali Alfageeh, Sadegh AlMahdi Kazemi Zarkouei, Daye Nam +9
Background and Context. The increasing integration of large language models (LLMs) in computing education presents an emerging challenge in understanding how students use LLMs and…
To Google or To ChatGPT? A Comparison of CS2 Students' Information Gathering Approaches and Outcomes
Aayush Kumar, Daniel Prol, Amin Alipour +1
LLMs such as ChatGPT have been widely adopted by students in higher education as tools for learning programming and related concepts. However, it remains unclear how effective stud…
Unlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code
Mahdi Kazemi, Aftab Hussain, Md Rafiqul Islam Rabin +2
This work investigates the application of Machine Unlearning (MU) for mitigating the impact of trojans embedded in conventional large language models of natural language (Text-LLMs…
Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing
Rabimba Karanjai, Aftab Hussain, Md Rafiqul Islam Rabin +3
Unit testing is crucial in software engineering for ensuring quality. However, it's not widely used in parallel and high-performance computing software, particularly scientific app…
Student-AI Interaction: A Case Study of CS1 students
Matin Amoozadeh, Daye Nam, Daniel Prol +5
The new capabilities of generative artificial intelligence tools Generative AI, such as ChatGPT, allow users to interact with the system in intuitive ways, such as simple conversat…
Revisiting Human Information Foraging: Adaptations for LLM-based Chatbots
Sruti Srinivasa Ragavan, Mohammad Amin Alipour
Information Foraging Theory's (IFT) framing of human information seeking choices as decision-theoretic cost-value judgments has successfully explained how people seek information a…
Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code
Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour
Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans.…
Trojans in Large Language Models of Code: A Critical Review through a Trigger-Based Taxonomy
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +3
Large language models (LLMs) have provided a lot of exciting new capabilities in software development. However, the opaque nature of these models makes them difficult to reason abo…
On Trojan Signatures in Large Language Models of Code
Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour
Trojan signatures, as described by Fields et al. (2021), are noticeable differences in the distribution of the trojaned class parameters (weights) and the non-trojaned class parame…
Measuring Impacts of Poisoning on Model Parameters and Neuron Activations: A Case Study of Poisoning CodeBERT
Aftab Hussain, Md Rafiqul Islam Rabin, Navid Ayoobi +1
Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans.…
TrojanedCM: A Repository of Trojaned Large Language Models of Code
Aftab Hussain, Md Rafiqul Islam Rabin, Mohammad Amin Alipour
With the rapid growth of research in trojaning deep neural models of source code, we observe that there is a need of developing a benchmark trojaned models for testing various troj…
Occlusion-based Detection of Trojan-triggering Inputs in Large Language Models of Code
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +2
Large language models (LLMs) are becoming an integrated part of software development. These models are trained on large datasets for code, where it is hard to verify each data poin…
Trust in Generative AI among students: An Exploratory Study
Matin Amoozadeh, David Daniels, Daye Nam +5
Generative artificial systems (GenAI) have experienced exponential growth in the past couple of years. These systems offer exciting capabilities, such as generating programs, that…
Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models
Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour
Neural code intelligence (CI) models are opaque black-boxes and offer little insight on the features they use in making predictions. This opacity may lead to distrust in their pred…
Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code
Md Rafiqul Islam Rabin, Mohammad Amin Alipour
There are several approaches for encoding source code in the input vectors of neural models. These approaches attempt to include various syntactic and semantic features of input pr…
Memorization and Generalization in Neural Code Intelligence Models
Md Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour +1
Deep Neural Networks (DNNs) are increasingly being used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generaliza…
Understanding Neural Code Intelligence Through Program Simplification
Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, Mohammad Amin Alipour
A wide range of code intelligence (CI) tools, powered by deep neural networks, have been developed recently to improve programming productivity and perform program analysis. To rel…
Configuring Test Generators using Bug Reports: A Case Study of GCC Compiler and Csmith
Md Rafiqul Islam Rabin, Mohammad Amin Alipour
The correctness of compilers is instrumental in the safety and reliability of other software systems, as bugs in compilers can produce executables that do not reflect the intent of…
Towards Demystifying Dimensions of Source Code Embeddings
Md Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali +1
Source code representations are key in applying machine learning techniques for processing and analyzing programs. A popular approach in representing source code is neural source c…
On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations
Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang +3
With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as pred…
Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations
Md Rafiqul Islam Rabin, Mohammad Amin Alipour
The abundance of publicly available source code repositories, in conjunction with the advances in neural networks, has enabled data-driven approaches to program analysis. These app…
Examining user reviews of conversational systems: a case study of Alexa skills
Soodeh Atefi, Andrew Truelove, Matheus Rheinschmitt +3
Conversational systems use spoken language to interact with their users. Although conversational systems, such as Amazon Alexa, are becoming common and afford interesting functiona…