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Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Saima Afrin, Alessandro Midolo, Camilo Escobar-Velásquez +5
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavi…
Quantize with Confidence? An Empirical Study of Quantization for Code Generation
Saima Afrin, Md. Zahidul Haque, Antonio Mastropaolo
The growing adoption of local inference frameworks such as Ollama has made it increasingly common for developers to run large code models on laptops and other resource-constrained…
Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization
Saima Afrin, Zaiyu Cheng, Tushar Sharma +3
The rapid advancement of Large Language Models (LLMs) has revolutionized software engineering automation, particularly in automated code summarization, which enhances program compr…
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…
Is Quantization a Deal-breaker? Empirical Insights from Large Code Models
Saima Afrin, Bowen Xu, Antonio Mastropaolo
The growing scale of large language models (LLMs) not only demands extensive computational resources but also raises environmental concerns due to their increasing carbon footprint…
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…
Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
Alessandro Giagnorio, Antonio Mastropaolo, Saima Afrin +2
Large Language Models (LLMs) have shown an impressive capability in code generation. The LLM effectiveness generally increases with its size: The higher the number of LLM's trainab…
Resource-Efficient & Effective Code Summarization
Saima Afrin, Joseph Call, Khai-Nguyen Nguyen +2
Code Language Models (CLMs) have demonstrated high effectiveness in automating software engineering tasks such as bug fixing, code generation, and code documentation. This progress…