CodeSAM: Source Code Representation Learning by Infusing Self-Attention with Multi-Code-View Graphs

TL;DR

CodeSAM introduces a flexible framework for enhancing transformer-based source code models by integrating multiple structural code-views through self-attention masks, leading to improved performance on key software engineering tasks.

Contribution

It presents a novel scalable method to infuse multiple code-views into transformer models via self-attention masks, improving downstream SE task performance.

Findings

Outperforms GraphCodeBERT and CodeBERT on code search, clone detection, and classification.

Utilizing multiple code-views enhances model effectiveness.

Enables resource-efficient, high-performing code representations.

Abstract

Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is largely attributed to the development of generalizable source code representations that effectively capture the syntactic and semantic characteristics of code. In recent years, pre-trained transformer-based models, inspired by natural language processing (NLP), have shown remarkable success in SE tasks. However, source code contains structural and semantic properties embedded within its grammar, which can be extracted from structured code-views like the Abstract Syntax Tree (AST), Data-Flow Graph (DFG), and Control-Flow Graph (CFG). These code-views can complement NLP techniques, further improving SE tasks. Unfortunately, there are no flexible frameworks to infuse arbitrary code-views into existing transformer-based models effectively. Therefore, in this work, we propose CodeSAM, a novel scalable framework to infuse multiple code-views into transformer-based models by creating self-attention masks. We use CodeSAM to fine-tune a small language model (SLM) like CodeBERT on the downstream SE tasks of semantic code search, code clone detection, and program classification. Experimental results show that by using this technique, we improve downstream performance when compared to SLMs like GraphCodeBERT and CodeBERT on all three tasks by utilizing individual code-views or a combination of code-views during fine-tuning. We believe that these results are indicative that techniques like CodeSAM can help create compact yet performant code SLMs that fit in resource constrained settings.