Leveraging Large Language Model for automatic patch correctness assessment

• Main Authors: ZHOU, Xin, XU, Bowen, KIM, Kisub, HAN, DongGyun, NGUYEN, Hung Huu, LE-CONG, Thanh, HE, Junda, LE, Bach, David LO
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Automatic patch correctness assessment; Large Language Models of code; In-context learning; Automated program repair; Artificial Intelligence and Robotics; Computer Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/9917
• Institution: Singapore Management University

Automated Program Repair (APR) techniques have shown more and more promising results in fixing real-world bugs. Despite the effectiveness, APR techniques still face an overfitting problem: a generated patch can be incorrect although it passes all tests. It is time-consuming to manually evaluate the...