collaborators

5 papers

cs.SE2026

A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher?

Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy

Transformer-based language models of code have achieved state-of-the-art performance across a wide range of software analytics tasks, but their practical deployment remains limited…

cs.SE2026

Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code

Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy

Transformer-based language models for code have shown remarkable performance in various software analytics tasks, but their adoption is hindered by high computational costs, slow i…

cs.SE2026

MoEKD: Mixture-of-Experts Knowledge Distillation for Robust and High-Performing Compressed Code Models

Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy

Large language models for code have achieved strong performance across diverse software analytics tasks, yet their real-world adoption remains limited by high computational demands…

cs.SE2025

Investigating Adversarial Attacks in Software Analytics via Machine Learning Explainability

MD Abdul Awal, Mrigank Rochan, Chanchal K. Roy

With the recent advancements in machine learning (ML), numerous ML-based approaches have been extensively applied in software analytics tasks to streamline software development and…

cs.SE2025

Large Language Models as Robust Data Generators in Software Analytics: Are We There Yet?

Md. Abdul Awal, Mrigank Rochan, Chanchal K. Roy

Large Language Model (LLM)-generated data is increasingly used in software analytics, but it is unclear how this data compares to human-written data, particularly when models are e…