Hyperparameter Optimization for AST Differencing
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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 still relies on default configurations or manual tweaking. In this paper we present a novel approach named DAT for hyperparameter optimization of AST differencing. We thoroughly state the problem of hyper configuration for AST differencing. We show that our data-driven approach to hyperoptimize AST differencing systems increases the edit-script quality in up to 53
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