5 papers · 1 filter
What Makes Software Bugs Escape Testing? Evidence from a Large-Scale Empirical Study
Domenico Cotroneo, Giuseppe De Rosa, Cristina Improta +1
Understanding how software defects manifest and evolve in production environments is critical for improving reliability. While previous research has largely focused on pre-release…
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…
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…
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…
Automating the Correctness Assessment of AI-generated Code for Security Contexts
Domenico Cotroneo, Alessio Foggia, Cristina Improta +2
Evaluating the correctness of code generated by AI is a challenging open problem. In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-…