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Source code analysis dataset.

Ben Gelman1, Banjo Obayomi1, Jessica Moore1

  • 1Machine Learning Group, Two Six Labs, 901 N. Stuart St, Suite 1000, Arlington, VA, 22203, USA.

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|November 26, 2019
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Summary
This summary is machine-generated.

This study links source code from GitHub projects with comments, build artifacts, and potential vulnerabilities. This dataset aids in code understanding, reverse engineering, and machine learning for vulnerability discovery.

Keywords:
Bug detectionCode commentsSource codeStatic analysis

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Area of Science:

  • Software Engineering
  • Computer Science
  • Data Science

Background:

  • Large-scale datasets linking source code to derived artifacts are crucial for advancing software analysis and development tools.
  • Existing resources often lack comprehensive pairings of code with comments, build outputs, or security vulnerability information.

Purpose of the Study:

  • To create a large-scale, multi-faceted dataset by pairing source code from GitHub projects with associated comments, build artifacts, and static analysis results.
  • To facilitate research in areas including automated code documentation, reverse engineering, and machine learning-based vulnerability detection.

Main Methods:

  • Collected 108,568 GitHub projects with redistributable licenses and at least 10 stars.
  • Generated code-comment pairs using Doxygen extraction for C, C++, Java, and Python.
  • Created code-build artifact pairs for C/C++ by executing the 'make' command.
  • Identified potential code vulnerabilities in C/C++ using the Infer static analyzer.

Main Results:

  • Successfully generated three distinct datasets: code-comment, code-build artifact, and code-vulnerability pairs.
  • The datasets encompass a wide range of programming languages and software development artifacts.
  • The generated data is suitable for diverse downstream machine learning and software engineering tasks.

Conclusions:

  • The curated dataset provides a valuable resource for advancing research in software comprehension, reverse engineering, and automated vulnerability discovery.
  • This work enables the development of more sophisticated tools for code analysis and security assessment through machine learning.