most citedApplying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models

2 citations · 2 across the 5 of their papers we have counts for

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cs.SE2026

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…

cs.SE2026

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…

cs.SE20252 cited

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…

cs.SE2025

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…

cs.SE20241 cited

Applying Large Language Models API to Issue Classification Problem

Gabriel Aracena, Kyle Luster, Fabio Santos +2

Effective prioritization of issue reports is crucial in software engineering to optimize resource allocation and address critical problems promptly. However, the manual classificat…