The paper addresses the tree-to-tree correction problem, which involves determining the distance between two labeled ordered trees \( T \) and \( T' \) by finding the minimum cost sequence of edit operations needed to transform \( T \) into \( T' \). The edit operations include changing one node into another, deleting a node, or inserting a node. An algorithm is presented that solves this problem in time \( O(V \cdot V' \cdot L^2 \cdot L'^2) \), where \( V \) and \( V' \) are the number of nodes in \( T \) and \( T' \), and \( L \) and \( L' \) are the maximum depths of \( T \) and \( T' \). The paper also discusses possible applications of this problem, such as measuring tree similarity, automatic error recovery and correction in programming languages, and finding the largest common substructure of two trees. The algorithm is derived from the concept of mappings, which describe how edit operations transform one tree into another, and it is shown that the distance between two trees can be computed by finding the minimum cost mapping. The paper concludes with a discussion on potential extensions and related problems, including the longest common subsequence problem and the possibility of extending the tree-to-tree correction problem by allowing node interchanging.The paper addresses the tree-to-tree correction problem, which involves determining the distance between two labeled ordered trees \( T \) and \( T' \) by finding the minimum cost sequence of edit operations needed to transform \( T \) into \( T' \). The edit operations include changing one node into another, deleting a node, or inserting a node. An algorithm is presented that solves this problem in time \( O(V \cdot V' \cdot L^2 \cdot L'^2) \), where \( V \) and \( V' \) are the number of nodes in \( T \) and \( T' \), and \( L \) and \( L' \) are the maximum depths of \( T \) and \( T' \). The paper also discusses possible applications of this problem, such as measuring tree similarity, automatic error recovery and correction in programming languages, and finding the largest common substructure of two trees. The algorithm is derived from the concept of mappings, which describe how edit operations transform one tree into another, and it is shown that the distance between two trees can be computed by finding the minimum cost mapping. The paper concludes with a discussion on potential extensions and related problems, including the longest common subsequence problem and the possibility of extending the tree-to-tree correction problem by allowing node interchanging.