Generating Synthetic Datasets for Evaluating Program Similarity Approaches

Core Concepts

This paper presents a framework for generating large, synthetic datasets with known ground truth program similarity to aid in the evaluation of novel program similarity approaches.

Abstract

The paper presents the HELIX framework for generating synthetic program datasets. HELIX allows for the combination of small, labeled components of program functionality to create samples with configurable similarity. The authors also introduce Blind HELIX, a tool that automatically extracts these functional components from existing open-source libraries using program slicing.
The authors evaluate their approach by comparing the performance of several popular program similarity tools on a manually-labeled dataset and a dataset generated using Blind HELIX. They find that the HELIX-generated dataset aligns well with the manually-labeled dataset, demonstrating that it can effectively model realistic notions of program similarity.
Key highlights:

HELIX is a framework for generating synthetic program datasets with known ground truth similarity.
Blind HELIX automatically extracts functional components from open-source libraries using program slicing.
Evaluation shows HELIX-generated datasets capture the same program similarity notions as a manually-labeled dataset.
HELIX and Blind HELIX are open-source and publicly available.

Stats

"Program similarity has become an increasingly popular area of research with various security applications such as plagiarism detection, author identification, and malware analysis."
"Few high-quality datasets for binary program similarity exist and are widely used in this domain."
"There are potentially many different, disparate definitions of what makes one program "similar" to another."

Quotes

"To combat the problem of poor dataset availability and quality, this paper describes our approach for generating synthetic program similarity datasets by slicing and recombining existing open-source libraries into samples with known, configurable ground truth similarity."
"We evaluate our approach against a manually-labeled dataset comprised of multiple abstract notions of program similarity."

Key Insights Distilled From

Synthetic Datasets for Program Similarity Research

by Alexander In... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03478.pdf

Deeper Inquiries

How could the HELIX framework be extended to generate datasets for other program analysis tasks beyond just similarity, such as vulnerability detection or program repair

The HELIX framework can be extended to generate datasets for other program analysis tasks by incorporating additional components and transformations that are specific to the task at hand. For vulnerability detection, HELIX could include components that represent common vulnerabilities such as buffer overflows, SQL injection, or cross-site scripting. These components could be combined in various ways to create samples that exhibit different types of vulnerabilities. Additionally, HELIX could incorporate transformations that introduce vulnerabilities into otherwise secure code, allowing researchers to evaluate vulnerability detection tools.
For program repair, HELIX could include components that represent common types of bugs or errors in code, such as null pointer dereferences, memory leaks, or logic errors. Researchers could then use HELIX to generate datasets of buggy code samples and corresponding repaired versions. By including components that represent both buggy and fixed code, researchers could evaluate program repair tools by measuring their ability to correctly identify and fix bugs in the samples.

What are the potential limitations or biases that could arise from relying solely on synthetic datasets generated by HELIX for evaluating program similarity approaches

Relying solely on synthetic datasets generated by HELIX for evaluating program similarity approaches may introduce limitations and biases. One potential limitation is that the synthetic datasets may not fully capture the complexity and variability of real-world programs. The components used to generate the datasets may not encompass all possible variations and nuances present in actual programs, leading to a limited representation of program similarity.
Biases could arise if the synthetic datasets are not diverse enough or if the components used to generate the datasets are biased towards certain types of programs or functionalities. For example, if the components predominantly represent a specific programming language or coding style, the generated datasets may not be representative of a broader range of programs. This could result in evaluation metrics favoring approaches that are better suited to the specific characteristics of the synthetic datasets.
To mitigate these limitations and biases, researchers should aim to incorporate a diverse set of components representing various programming languages, libraries, and coding styles into HELIX. Additionally, researchers should validate the synthetic datasets by comparing them to real-world datasets to ensure that they accurately reflect the complexities and variations present in actual programs.

How might the HELIX framework be adapted to generate datasets that capture more complex, higher-level notions of program semantics and behavior beyond just structural similarity

To adapt the HELIX framework to generate datasets that capture more complex, higher-level notions of program semantics and behavior beyond just structural similarity, researchers can introduce components and transformations that represent semantic features and behavioral patterns in code.
For capturing program semantics, HELIX could include components that represent high-level programming constructs such as classes, functions, and control flow structures. By combining these components in different ways, researchers can generate samples that exhibit varying levels of semantic similarity based on the shared semantic features.
For capturing program behavior, HELIX could incorporate components that represent common patterns of program execution, such as loops, conditionals, and function calls. These components could be used to create samples that exhibit similar behavioral characteristics, allowing researchers to evaluate program similarity based on how the code behaves at runtime.
By expanding the range of components and transformations in HELIX to encompass both structural and semantic aspects of programs, researchers can generate datasets that provide a more comprehensive and nuanced understanding of program similarity beyond just code structure.