From the 1 of 3 linked papers with an AI index.
3 papers
cs.SE2026
Quantize with Confidence? An Empirical Study of Quantization for Code Generation
Saima Afrin, Md. Zahidul Haque, Antonio Mastropaolo
The paper empirically evaluates six post‑training quantization methods for large code generation models, measuring their effects on functional correctness, code quality, and robust…
cs.SE2026
Parameter-Efficient Multi-Task Fine-Tuning in Code-Related Tasks
Md Zahidul Haque, Saima Afrin, Antonio Mastropaolo
Large Language Models (LLMs) have proven highly effective in automating software engineering tasks, bridging natural language and code semantics to achieve notable results in code…
cs.SE2025
A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models
Saima Afrin, Md Zahidul Haque, Antonio Mastropaolo
The rise of Artificial Intelligence (AI)-and particularly Large Language Models (LLMs) for code-has reshaped Software Engineering (SE) by enabling the automation of tasks such as c…