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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE)

DOI: 10.1145/3742876.3742881

Authors: Iman Hemati Moghadam, Oebele Lijzenga, Vadim Zaytsev

Institutions: Eindhoven University of Technology, University of Twente

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4411723933

FWCI:

Cites: 51

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Related to: 10