A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms

TL;DR

This paper introduces a hierarchical differential testing approach to evaluate the accuracy of AST mapping algorithms, revealing significant inaccuracies in widely used algorithms through expert validation and large-scale analysis.

Contribution

It proposes a novel hierarchical comparison method for AST mappings and provides the first large-scale evaluation of popular algorithms' accuracy.

Findings

High precision (0.98-1.00) in expert validation

Significant inaccuracy rates (20-36%) in existing algorithms

Demonstrates need for improving AST mapping algorithms

Abstract

Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorihtm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if each of the compared algorithms generates inaccurate mappings for a statement or its tokens. We invite 12 external experts to determine if three commonly used AST mapping algorithms generate accurate mappings for a statement and its tokens for 200 statements. Based on the experts' feedback,we observe that our approach achieves a precision of 0.98--1.00 and a recall of 0.65--0.75. Furthermore, we conduct a large-scale study with a dataset of ten Java projects, containing a total of 263,165 file revisions. Our approach determines that GumTree, MTDiff and IJM generate inaccurate mappings for 20%--29%, 25%--36% and 21%--30% of the file revisions, respectively. Our experimental results show that state-of-art AST mapping agorithms still need improvements.