5 papers
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…
Detecting Stealthy Data Poisoning Attacks in AI Code Generators
Cristina Improta
Deep learning (DL) models for natural language-to-code generation have become integral to modern software development pipelines. However, their heavy reliance on large amounts of d…
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 Robustness of AI Offensive Code Generators via Data Augmentation
Cristina Improta, Pietro Liguori, Roberto Natella +2
Since manually writing software exploits for offensive security is time-consuming and requires expert knowledge, AI-base code generators are an attractive solution to enhance secur…