  Computing the differences between two versions of the same program is an
essential task for software development and software evolution research. AST
differencing is the most advanced way of doing so, and an active research area.
Yet, AST differencing algorithms rely on configuration parameters that may have
a strong impact on their effectiveness. In this paper, we present a novel
approach named DAT (Diff Auto Tuning) for hyperparameter optimization of AST
differencing. We thoroughly state the problem of hyper-configuration for AST
differencing. We evaluate our data-driven approach DAT to optimize the
edit-scripts generated by the state-of-the-art AST differencing algorithm named
GumTree in different scenarios. DAT is able to find a new configuration for
GumTree that improves the edit-scripts in 21.8% of the evaluated cases.
