TGMM: Combining Parse Tree with GPU for Multilingual and Multi-Granularity Code Clone Detection

Core Concepts

TGMM introduces a novel approach to code clone detection by combining parse trees with GPU acceleration, achieving high precision and execution speed.

Abstract

• TGMM proposes a tree and GPU-based tool for multilingual and multi-granularity code clone detection.
• The tool extracts code blocks at specified granularities and efficiently detects Type-3 clones.
• TGMM outperforms existing tools in terms of execution time and precision, with comparable recall rates.
• The tool supports 25 out of 30 mainstream programming languages, with extensibility for personalized needs.
• TGMM's performance is evaluated on BigCloneBench dataset, showcasing high recall and precision across different clone types.
• The tool's execution time is competitive, especially on large-scale inputs, outperforming other state-of-the-art tools.
• TGMM's multilingual support is comprehensive, with compatibility for various languages and granularities.

Stats

TGMM ranks first in execution time and precision, while maintaining comparable recall rates.
TGMM supports 25 mainstream programming languages.
TGMM detected 100 MLOC input in just two hours, outperforming other tools.

Quotes

"TGMM introduces a novel approach to code clone detection by combining parse trees with GPU acceleration."
"TGMM outperforms existing tools in terms of execution time and precision."

Key Insights Distilled From

TGMM

by Yuhang Ye,Yu... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18202.pdf

Deeper Inquiries

How does TGMM's approach of combining parse trees with GPU acceleration impact the scalability of code clone detection?

TGMM's approach of combining parse trees with GPU acceleration significantly impacts the scalability of code clone detection. By generating parse trees based on user-provided grammar files and then transforming them into node sequences, TGMM can efficiently extract code blocks at a specified granularity. The utilization of GPU enables parallel processing of code clone detection tasks, leading to a substantial speedup compared to traditional CPU-based approaches. This parallel processing capability allows TGMM to handle large-scale codebases with millions of lines of code, making it highly scalable. The GPU acceleration enhances the speed and efficiency of generating suffix arrays and processing large amounts of data, ultimately improving the scalability of code clone detection in TGMM.

What are the potential limitations of TGMM's support for multilingual clone detection?

While TGMM offers extensive support for multilingual clone detection by leveraging ANTLRv4 grammar files, there are potential limitations to consider. One limitation is the dependency on the availability and accuracy of grammar files for different languages. If a specific language lacks a comprehensive or up-to-date grammar file, TGMM may struggle to accurately parse and detect clones in that language. Additionally, the complexity and diversity of language syntax across various programming languages can pose challenges for ensuring consistent and reliable clone detection results. TGMM's effectiveness in multilingual clone detection may also be influenced by the quality of the grammar files and the ability to handle unique language features or constructs. Furthermore, the performance of TGMM in detecting complex clones across multiple languages may vary, depending on the intricacies of each language's syntax and structure.

How can the concept of data grouping in TGMM be applied to enhance the efficiency of other code analysis tasks?

The concept of data grouping in TGMM, where the concatenated subtree sequence is divided into multiple groups for parallel processing, can be applied to enhance the efficiency of other code analysis tasks. By dividing the data into manageable chunks and processing them in parallel, tasks that involve large datasets or complex computations can benefit from improved performance and reduced processing times. This approach can be particularly useful for tasks such as static code analysis, bug detection, program comprehension, and optimization analysis. Implementing data grouping in other code analysis tools can help distribute the computational workload, optimize resource utilization, and accelerate the overall analysis process. Additionally, data grouping can facilitate scalability and enable the analysis of larger codebases without compromising efficiency.