23 citations · 51 across the 8 of their papers we have counts for
11 papers
Will It Break in Production? Metric-Driven Prediction of Residual Defects in Python Systems
Giuseppe De Rosa, Pietro Liguori
Python's dynamic nature complicates testing and increases the possibility that some defects evade detection, so an effective fault prediction becomes essential. We examine whether…
Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity
Domenico Cotroneo, Cristina Improta, Pietro Liguori
As AI code assistants become increasingly integrated into software development workflows, understanding how their code compares to human-written programs is critical for ensuring r…
PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection
Domenico Cotroneo, Giuseppe De Rosa, Pietro Liguori
This paper presents PyResBugs, a curated dataset of residual bugs, i.e., defects that persist undetected during traditional testing but later surface in production, collected from…
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…
CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection
Ruijun Feng, Hammond Pearce, Pietro Liguori +1
Large language models (LLMs) have been proposed as powerful tools for detecting software vulnerabilities, where task-specific fine-tuning is typically employed to provide vulnerabi…
Enhancing AI-based Generation of Software Exploits with Contextual Information
Pietro Liguori, Cristina Improta, Roberto Natella +2
This practical experience report explores Neural Machine Translation (NMT) models' capability to generate offensive security code from natural language (NL) descriptions, highlight…