Integrated Reasoning Engine for Pointer-related Code Clone Detection

TL;DR

Twin-Finder+ is a novel approach combining machine learning and symbolic execution to accurately detect pointer-related code clones, significantly reducing false positives and uncovering previously unreported bugs.

Contribution

It introduces a formal verification mechanism into clone detection, enhancing precision and automating manual review processes.

Findings

Removes 91.69% false positives on average

Detects 6 unreported bugs in real-world applications

Identifies a patched bug in LibreOffice

Abstract

Detecting similar code fragments, usually referred to as code clones, is an important task. In particular, code clone detection can have significant uses in the context of vulnerability discovery, refactoring and plagiarism detection. However, false positives are inevitable and always require manual reviews. In this paper, we propose Twin-Finder+, a novel closed-loop approach for pointer-related code clone detection that integrates machine learning and symbolic execution techniques to achieve precision. Twin-Finder+ introduces a formal verification mechanism to automate such manual reviews process. Our experimental results show Twin-Finder+ that can remove 91.69% false positives in average. We further conduct security analysis for memory safety using real-world applications, Links version 2.14 and libreOffice-6.0.0.1. Twin-Finder+ is able to find 6 unreported bugs in Links version 2.14 and one public patched bug in libreOffice-6.0.0.1.