Comparative Analysis of Pre-trained Code Language Models for Automated Program Repair via Code Infill Generation

Automated Program Repair (APR) has advanced significantly with the emergence of pre-trained Code Language Models (CLMs), enabling the generation of high-quality patches. However, selecting the most suitable CLM for APR remains challenging due to a range of factors, including accuracy, efficiency, and scalability, among others. These factors are interdependent and interact in complex ways, making the selection of a CLM for APR a multifaceted problem.