5 papers · 1 filter
Characterizing Software Aging in GPU-Based LLM Serving Systems
Domenico Cotroneo, Bojan Cukic
This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric software with relatively regul…
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…
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…