Comparison of Clang Abstract Syntax Trees using String Kernels

Abstract Syntax Trees (ASTs) are intermediate representations widely used by compiler frameworks. One of their strengths is that they can be used to determine the similarity among a collection of programs. In this paper we propose a novel comparison method that converts ASTs into weighted strings in order to get similarity matrices and quantify the level of correlation among codes. To evaluate the approach, we leveraged the corresponding strings derived from the Clang ASTs of a set of 100 source code examples written in C. Our kernel and two other string kernels from the literature were used to obtain similarity matrices among those examples. Next, we used Hierarchical Clustering to visualize the results. Our solution was able to identify different clusters conformed by examples that shared similar semantics. We demonstrated that the proposed strategy can be promisingly applied to similarity problems involving trees or strings.

I. Introduction

Computer programs exhibit similarities that can be detected before, during, and after execution time. Being similar means sharing syntactical or semantical structure in a significant proportion. Programs that are similar tend to behave in similar manner too. This can be utilized, for example, in the analysis and improvement of the overall performance of a set of programs by focusing on finding patterns that behave similar but have a different performance. The detection of program similarities has been identified as an emerging topic in software engineering areas [1].