An algorithm to determine the extended edit distance between program codes is presented. In addition to the conventional Levenshtein distance, the extended edit distance considers some common operations to a program code to find similar programs more accurately. To calculate the distance, the algorithm employs dynamic programming techniques as well as an algorithm for solving the minimum cost flow on a bipartite graph. In this paper, details of the algorithm and experimental results are presented. These experiments were conducted with source code submitted to an online judge system, where a number of source codes for each programming problem are located. The results show that the proposed algorithm can find source code that cannot be found by the conventional Levenshtein distance, with a higher probability.