2 citations · 2 across the 5 of their papers we have counts for
5 papers
Prompt Structure Redistributes, Not Reduces: An Empirical Analysis of Security-Weaknesses in LLM-Generated Python Code
Maitreyee Das Urmi, Jessica Pourleyli, Fabio Santos +1
Large Language Models (LLMs) increasingly generate code from natural-language prompts, making prompt engineering a key mechanism for shaping the security of generated software. Str…
Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency
Junchi Liu, Ali Bigdeli, Roya Daneshi +3
Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques. While Large Language Model (LLM)-based APR…
Does Fixing Break Security? An Empirical Study of Security Degradation in Iterative LLM-Driven Infrastructure-as-Code Repair
Benjamin Agyekum, Fabio Santos
Background: Iterative feedback loops are the dominant paradigm for improving LLM-generated Infrastructure-as-Code (IaC): validators such as Checkov and terraform validate feed erro…
Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Gabriel Aracena, Kyle Luster, Fabio Santos +2
Effective prioritization of issue reports in software engineering helps to optimize resource allocation and information recovery. However, manual issue classification is laborious…
SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub
Benjamin C. Carter, Jonathan Rivas Contreras, Carlos A. Llanes Villegas +9
New contributors often struggle to find tasks that they can tackle when onboarding onto a new Open Source Software (OSS) project. One reason for this difficulty is that issue track…