3 citations · 6 across the 17 of their papers we have counts for
18 papers
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…
Developers and Generative AI: A Study of Self-Admitted Usage in Open Source Projects
Rosalia Tufano, Federica Pepe, Fiorella Zampetti +4
The availability of generative Artificial Intelligence (AI) tools such as ChatGPT or GitHub Copilot is reshaping the way in which software is developed, evolved, and maintained. Of…
Studying Quality Improvements Recommended via Manual and Automated Code Review
Giuseppe Crupi, Rosalia Tufano, Gabriele Bavota
Several Deep Learning (DL)-based techniques have been proposed to automate code review. Still, it is unclear the extent to which these approaches can recommend quality improvements…
Improving Code Generation via Small Language Model-as-a-judge
Giuseppe Crupi, Rosalia Tufano, Gabriele Bavota
Large language models (LLMs) have shown remarkable capabilities in automated code generation. While effective for mainstream languages, they may underperform on less common or doma…
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…
SATORI: Static Test Oracle Generation for REST APIs
Juan C. Alonso, Alberto Martin-Lopez, Sergio Segura +2
REST API test case generation tools are evolving rapidly, with growing capabilities for the automated generation of complex tests. However, despite their strengths in test data gen…
On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
Giuseppe Crupi, Rosalia Tufano, Alejandro Velasco +3
Large Language Models have been recently exploited as judges for complex natural language processing tasks, such as Q&A. The basic idea is to delegate to an LLM the assessment of t…
Leveraging Reward Models for Guiding Code Review Comment Generation
Oussama Ben Sghaier, Rosalia Tufano, Gabriele Bavota +1
Code review is a crucial component of modern software development, involving the evaluation of code quality, providing feedback on potential issues, and refining the code to addres…
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…
Quality In, Quality Out: Investigating Training Data's Role in AI Code Generation
Cristina Improta, Rosalia Tufano, Pietro Liguori +2
Deep Learning-based code generators have seen significant advancements in recent years. Tools such as GitHub Copilot are used by thousands of developers with the main promise of a…
Automating Code Review: A Systematic Literature Review
Rosalia Tufano, Gabriele Bavota
Code Review consists in assessing the code written by teammates with the goal of increasing code quality. Empirical studies documented the benefits brought by such a practice that,…
Investigating Execution-Aware Language Models for Code Optimization
Federico Di Menna, Luca Traini, Gabriele Bavota +1
Code optimization is the process of enhancing code efficiency, while preserving its intended functionality. This process often requires a deep understanding of the code execution b…
Enhancing Code Generation for Low-Resource Languages: No Silver Bullet
Alessandro Giagnorio, Alberto Martin-Lopez, Gabriele Bavota
The advent of Large Language Models (LLMs) has significantly advanced the field of automated code generation. LLMs rely on large and diverse datasets to learn syntax, semantics, an…
Deep Learning-based Code Completion: On the Impact on Performance of Contextual Information
Matteo Ciniselli, Luca Pascarella, Gabriele Bavota
Code completion aims at speeding up code writing by recommending to developers the next tokens they are likely to type. Deep Learning (DL) models pushed the boundaries of code comp…
On the Generalizability of Transformer Models to Code Completions of Different Lengths
Nathan Cooper, Rosalia Tufano, Gabriele Bavota +1
The programming landscape is nowadays being reshaped by the advent of Large Language Models (LLMs) able to automate code-related tasks related to code implementation (e.g., code co…
Deep Learning-based Code Reviews: A Paradigm Shift or a Double-Edged Sword?
Rosalia Tufano, Alberto Martin-Lopez, Ahmad Tayeb +3
Several techniques have been proposed to automate code review. Early support consisted in recommending the most suited reviewer for a given change or in prioritizing the review tas…
SEART Data Hub: Streamlining Large-Scale Source Code Mining and Pre-Processing
Ozren Dabić, Rosalia Tufano, Gabriele Bavota
Large-scale code datasets have acquired an increasingly central role in software engineering (SE) research. This is the result of (i) the success of the mining software repositorie…