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Automated program repair (APR) using deep learning techniques has become animportant area of research in recent years, aiming to automatically generatebug-fixing patches that can improve software reliability and maintainability.However, most existing methods either target a single language or require highcomputational resources to train multilingual models. In this paper, we proposeT5APR, a novel neural program repair approach that provides a unified solutionfor bug fixing across multiple programming languages. T5APR leverages CodeT5, apowerful pre-trained text-to-text transformer model, and adopts a checkpointensemble strategy to improve patch recommendation. We conduct comprehensiveevaluations on six well-known benchmarks in four programming languages (Java,Python, C, JavaScript), demonstrating T5APR's competitiveness againststate-of-the-art techniques. T5APR correctly fixes 1,985 bugs, including 1,442bugs that none of the compared techniques has fixed. We further support theeffectiveness of our approach by conducting detailed analyses, such ascomparing the correct patch ranking among different techniques. The findings ofthis study demonstrate the potential of T5APR for use in real-worldapplications and highlight the importance of multilingual approaches in thefield of APR.

T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble