25 citations · 58 across the 19 of their papers we have counts for
19 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.…
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…
Readle: A Formal Framework for Designing AI-based Edge Systems
Aftab Hussain
With the wide spread use of AI-driven systems in the edge (a.k.a edge intelligence systems), such as autonomous driving vehicles, wearable biotech devices, intelligent manufacturin…
Testing the Robustness of a BiLSTM-based Structural Story Classifier
Aftab Hussain, Sai Durga Prasad Nanduri, Sneha Seenuvasavarathan
The growing prevalence of counterfeit stories on the internet has fostered significant interest towards fast and scalable detection of fake news in the machine learning community.…
DIAR: Removing Uninteresting Bytes from Seeds in Software Fuzzing
Aftab Hussain, Mohammad Amin Alipour
Software fuzzing mutates bytes in the test seeds to explore different behaviors of the program under test. Initial seeds can have great impact on the performance of a fuzzing campa…
FMViz: Visualizing Tests Generated by AFL at the Byte-level
Aftab Hussain, Mohammad Amin Alipour
Software fuzzing is a strong testing technique that has become the de facto approach for automated software testing and software vulnerability detection in the industry. The random…
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…
A holistic look at requirements engineering practices in the gaming industry
Aftab Hussain, Omar Asadi, Debra J. Richardson
In this work we present an account of the status of requirements engineering in the gaming industry. Recent papers in the area were surveyed. Characterizations of the gaming indust…