Pruning the AST with Hunks to Speed up Tree Differencing
Chunhua Yang, E. James Whitehead · 2019
Inefficiency is a problem in tree-differencing approaches. As textual-differencing approaches are highly efficient and the hunks returned by them reflect the line range of the modified code, we propose a novel approach to prune the AST with hunks. We define the pruning strategies at the declaration level and the statement level, respectively. And, we have designed an algorithm to implement the strategies. Furthermore, we have integrated the algorithm to Change Distiller and GumTree. Through an evaluation on four open source projects, the results show that the approach is very effective in reducing the number of nodes and shortening the running time. On average, with declaration-level pruning, the number of nodes in the two tools is reduced by at least 64%. With statement-level pruning, the number of nodes in both tools is reduced by at least 74%. By using the declaration-level pruning and the statement-level pruning, GumTree's runtime is reduced by at least 70% and 75%, respectively.