This document summarizes a talk given about the most influential paper award from ICSE2023 on program repair and auto-coding. It discusses: 1. The 2013 SemFix paper which introduced an automated repair method using symbolic execution, constraint solving, and program synthesis to generate patches without formal specifications. 2. How subsequent work incorporated learning and inference techniques to glean specifications from tests to guide repair when specifications were not available. 3. The impact of machine learning approaches on automated program repair, including learning from large code change datasets to predict edits, and opportunities for continued improvement in localization and accuracy.