5 citations · 6 across the 5 of their papers we have counts for
6 papers
Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.js
Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu +5
Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical challenge: how can security analysts p…
Adversarial Reasoning for Repair Based on Inferred Program Intent
He Ye, Aidan Z. H. Yang, Chang Hu +3
Automated program repair (APR) has shown promising results, particularly with the use of neural networks. Currently, most APR tools focus on code transformations specified by test…
Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Aidan Z. H. Yang, Haoye Tian, He Ye +2
Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanc…
Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models
Aidan Z. H. Yang, Sophia Kolak, Vincent J. Hellendoorn +2
Language models have improved by orders of magnitude with the recent emergence of Transformer-based Large Language Models (LLMs). LLMs have demonstrated their ability to generate n…
On Repairing Natural Language to SQL Queries
Aidan Z. H. Yang, Ricardo Brancas, Pedro Esteves +5
Data analysts use SQL queries to access and manipulate data on their databases. However, these queries are often challenging to write, and small mistakes can lead to unexpected dat…
Large Language Models for Test-Free Fault Localization
Aidan Z. H. Yang, Ruben Martins, Claire Le Goues +1
Fault Localization (FL) aims to automatically localize buggy lines of code, a key first step in many manual and automatic debugging tasks. Previous FL techniques assume the provisi…