5 papers
Unreliable in Practice? A Comprehensive Study of Errors in LLM-Generated Code
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos
Large Language Models (LLMs) are being widely used for coding, with reports indicating that AI now generates an increasing share of production code. Studies show that LLMs can sign…
PROBE: Benchmarking Code Generation in Large Language Models
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos
Large Language Models (LLMs) are increasingly being used in everyday software engineering tasks, particularly in automated code generation. Despite their widespread adoption, these…
Effectiveness of LLM-based Software Diversity for Reliability Improvement -- an Empirical Study
Gabriel Almeida, Ilir Gashi, Vladimir Stankovic +1
Software diversity has been extensively studied as a means of reducing the risk of common-mode failures. Classic work showed that the central issue is whether failures of diversely…
A Systematic Methodology for Evaluating Failure Independence in LLM-Generated Code
Rodrigo Pato Nogueira, Karthik Pattabiraman, Marco Vieira +1
N-Version Programming (NVP) improves software reliability by executing multiple independent implementations and combining outputs, but its adoption is limited by high cost and the…
Leveraging Large Language Models for Trustworthiness Assessment of Web Applications
Oleksandr Yarotskyi, José D'Abruzzo Pereira, João R. Campos
The widespread adoption of web applications has made their security a critical concern and has increased the need for systematic ways to assess whether they can be considered trust…