# Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks

arXiv: https://arxiv.org/abs/2410.05275 · HTML: https://mlsanity.com/paper/2410.05275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [How Small Transformation Expose the Weakness of Semantic Similarity Measures](https://mlsanity.com/paper/2509.09714/) (2025)

## Abstract (arXiv)

Assessing the degree of similarity of code fragments is crucial for ensuring software quality, but it remains challenging due to the need to capture the deeper semantic aspects of code. Traditional syntactic methods often fail to identify these connections. Recent advancements have addressed this challenge, though they frequently sacrifice interpretability. To improve this, we present an approach aiming to improve the transparency of the similarity assessment by using GraphCodeBERT, which enables the identification of semantic relationships between code fragments. This approach identifies similar code fragments and clarifies the reasons behind that identification, helping developers better understand and trust the results. The source code for our implementation is available at https://www.github.com/jorge-martinez-gil/graphcodebert-interpretability.