92 citations · 242 across the 25 of their papers we have counts for
25 papers
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…
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…
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.…
Calibration and Correctness of Language Models for Code
Claudio Spiess, David Gros, Kunal Suresh Pai +6
Machine learning models are widely used, but can also often be wrong. Users would benefit from a reliable indication of whether a given output from a given model should be trusted,…
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…
A Survey of Trojans in Neural Models of Source Code: Taxonomy and Techniques
Aftab Hussain, Md Rafiqul Islam Rabin, Toufique Ahmed +4
In this work, we study literature in Explainable AI and Safe AI to understand poisoning of neural models of code. In order to do so, we first establish a novel taxonomy for Trojan…
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Aftab Hussain, Md Rafiqul Islam Rabin, Bowen Xu +2
Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the…
Study of Distractors in Neural Models of Code
Md Rafiqul Islam Rabin, Aftab Hussain, Sahil Suneja +1
Finding important features that contribute to the prediction of neural models is an active area of research in explainable AI. Neural models are opaque and finding such features sh…
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…
Extracting Label-specific Key Input Features for Neural Code Intelligence Models
Md Rafiqul Islam Rabin
The code intelligence (CI) models are often black-box and do not offer any insights on the input features that they learn for making correct predictions. This opacity may lead to d…
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…
COVID-19: Social Media Sentiment Analysis on Reopening
Mohammed Emtiaz Ahmed, Md Rafiqul Islam Rabin, Farah Naz Chowdhury
The novel coronavirus (COVID-19) pandemic is the most talked topic in social media platforms in 2020. People are using social media such as Twitter to express their opinion and sha…
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…
Testing Neural Program Analyzers
Md Rafiqul Islam Rabin, Ke Wang, Mohammad Amin Alipour
Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug predi…