一句话：提出基于机器学习的工具 CloneCognition，自动验证代码克隆检测结果，减少人工确认。
代码克隆检测工具为处理复杂代码结构，常对源码做大量泛化，导致报告许多仅偶然相似、用户并不认为是克隆的代码片段，需要人工验证，耗时且困难。本文提出基于机器学习的代码克隆验证工具 CloneCognition，可运行于任意克隆检测工具之上，自动完成原本繁琐的人工验证过程：对检测到的候选克隆对进行分类，判断其是否为真实克隆。评估显示，其克隆分类准确率最高达 87.4%，与现有最先进的代码克隆验证技术相比取得显著提升，表明机器学习可有效自动化克隆验证。
A code clone is a pair of similar code fragments, within or between software systems. To detect each possible clone pair from a software system while handling the complex code structures, the clone detection tools undergo a lot of generalization of the original source codes. The generalization often results in returning code fragments that are only coincidentally similar and not considered clones by users, and hence requires manual validation of the reported possible clones by users which is often both time-consuming and challenging. In this paper, we propose a machine learning based tool 'CloneCognition' (Open Source Codes: https://github.com/pseudoPixels/CloneCognition ; Video Demonstration: https://www.youtube.com/watch?v=KYQjmdr8rsw) to automate the laborious manual validation process. The tool runs on top of any code clone detection tools to facilitate the clone validation process. The tool shows promising clone classification performance with an accuracy of up to 87.4%. The tool also exhibits significant improvement in the results when compared with state-of-the-art techniques for code clone validation.
DOI 原文 ·
@inproceedings{paperbot5075,
title = {CloneCognition: machine learning based code clone validation tool},
author = {Golam Mostaeen and Jeffrey Svajlenko and Banani Roy and Chanchal K. Roy and Kevin A. Schneider},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3341182}
}