Improving Source Code Similarity Detection Through GraphCodeBERT and Integration of Additional Features

Abstract

This paper investigates source code similarity detection using a transformer model augmented with an execution-derived signal. We extend GraphCodeBERT with an explicit, low-dimensional behavioral feature that captures observable agreement between code fragments, and fuse this signal with the pooled transformer representation through a trainable output head. We compute behavioral agreement via output comparisons under a fixed test suite and use this observed output agreement as an operational approximation of semantic similarity between code pairs. The resulting feature acts as an explicit behavioral signature that complements token- and graph-based representations. Experiments on established clone detection benchmarks show consistent improvements in precision, recall, and F$_1$ over the unmodified GraphCodeBERT baseline, with the largest gains on semantically equivalent but syntactically dissimilar pairs. The source code that illustrates our approach can be downloaded from https://www.github.com/jorge-martinez-gil/graphcodebert-feature-integration.