6 papers
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…
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…
Tratto: A Neuro-Symbolic Approach to Deriving Axiomatic Test Oracles
Davide Molinelli, Alberto Martin-Lopez, Elliott Zackrone +3
This paper presents Tratto, a neuro-symbolic approach that generates assertions (boolean expressions) that can serve as axiomatic oracles, from source code and documentation. The s…
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…
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 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…