AutoTransform: Automated Code Transformation to Support Modern Code Review Process

Abstract

Code review is effective, but human-intensive (e.g., developers need to manually modify source code until it is approved). Recently, prior work proposed a Neural Machine Translation (NMT) approach to automatically transform source code to the version that is reviewed and approved (i.e., the after version). Yet, its performance is still suboptimal when the after version has new identifiers or literals (e.g., renamed variables) or has many code tokens. To address these limitations, we propose AutoTransform which leverages a Byte-Pair Encoding (BPE) approach to handle new tokens and a Transformer-based NMT architecture to handle long sequences. We evaluate our approach based on 14,750 changed m..