activity
20182025
most citedMemorization and Generalization in Neural Code Intelligence Models

25 citations · 58 across the 19 of their papers we have counts for

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Showing cs.SEShow all

14 papers · 1 filter

cs.SE2025

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…

cs.SE2024

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…

cs.SE2024

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…

cs.SE2024★ 2 cited

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.…

cs.SE2024★ 2 cited

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…

cs.SE2024★ 2 cited

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.…

cs.SE2023★ 3 cited

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…

cs.SE2023★ 2 cited

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…

cs.SE2023★ 4 cited

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…

cs.SE2022★ 12 cited

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…

cs.SE2022

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…

cs.SE2021

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…

cs.SE2021★ 1 cited

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…

cs.SE2018★ 1 cited

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…