92 citations · 242 across the 25 of their papers we have counts for
18 papers · 1 filter
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…
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…
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…
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…
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…
K-CONFIG: Using Failing Test Cases to Generate Test Cases in GCC Compilers
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 programs that do not reflect the intents of p…