33 citations · 58 across the 15 of their papers we have counts for
16 papers
What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code
Cristina Improta, Pietro Liguori, Domenico Cotroneo
AI coding assistants are becoming co-authors of production software, yet their evaluation centers on functional correctness, leaving open whether their code differs from human code…
Beyond Fixed Fault Models: Comparing LLM-Based and Rule-Based Fault Injection in OpenStack
Giuseppe De Rosa, Pietro Liguori, Domenico Cotroneo
Software Fault Injection (SFI) supports testing of cloud systems by introducing software defects and observing their manifestation. Rule-based injectors such as ProFIPy provide con…
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…
Willing but Unable: Separating Refusal from Capability in Code LLMs via Abliteration
Cristina Carleo, Pietro Liguori, Naghmeh Ivaki +1
Producing a labeled vulnerable code at scale is a recurring obstacle for learning-based vulnerability detection: mined corpora carry substantial label noise, and existing LLM-based…
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…
Elevating Cyber Threat Intelligence against Disinformation Campaigns with LLM-based Concept Extraction and the FakeCTI Dataset
Domenico Cotroneo, Roberto Natella, Vittorio Orbinato
The swift spread of fake news and disinformation campaigns poses a significant threat to public trust, political stability, and cybersecurity. Traditional Cyber Threat Intelligence…
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…
Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?
D. Cotroneo, F. C. Grasso, R. Natella +1
Vulnerability prediction is valuable in identifying security issues efficiently, even though it requires the source code of the target software system, which is a restrictive hypot…
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…
Neural Fault Injection: Generating Software Faults from Natural Language
Domenico Cotroneo, Pietro Liguori
Traditional software fault injection methods, while foundational, face limitations in adequately representing real-world faults, offering customization, and requiring significant m…
DeVAIC: A Tool for Security Assessment of AI-generated Code
Domenico Cotroneo, Roberta De Luca, Pietro Liguori
Context: AI code generators are revolutionizing code writing and software development, but their training on large datasets, including potentially untrusted source code, raises sec…
AI Code Generators for Security: Friend or Foe?
Roberto Natella, Pietro Liguori, Cristina Improta +2
Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors. We review use cas…
Laccolith: Hypervisor-Based Adversary Emulation with Anti-Detection
Vittorio Orbinato, Marco Carlo Feliciano, Domenico Cotroneo +1
Advanced Persistent Threats (APTs) represent the most threatening form of attack nowadays since they can stay undetected for a long time. Adversary emulation is a proactive approac…
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-…