7 papers
Writing Bug Reports for Software Repair Agents: What Information Matters Most?
Vincenzo Luigi Bruno, Alessandro Giagnorio, Daniele Bifolco +3
Software development is increasingly moving toward agentic-first workflows. This includes AI agents responsible for generating initial fixes for submitted issue reports. In this se…
Teaching LLMs a Low-Resource Language: Enhancing Code Completion in Pharo
Kilian Kier, Alessandro Giagnorio, Omar AbedelKader +5
Large Language Models (LLMs) unlocked new possibilities in automated code writing, becoming the backbone of most code completion tools. While LLMs excel in mainstream languages, th…
No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages
Alessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota
Large Language Models (LLMs) have significantly advanced the automation of software engineering tasks. One prominent example is code generation, where an LLM produces code in a spe…
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…
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
Alessandro Midolo, Alessandro Giagnorio, Fiorella Zampetti +3
Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generation. Previous research has shown how suitable prom…
Why Personalizing Deep Learning-Based Code Completion Tools Matters
Alessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota
Deep learning (DL)-based code completion tools have transformed software development by enabling advanced code generation. These tools leverage models trained on vast amounts of co…