| 摘要: |
| 内存泄漏在采用显式内存管理机制的C语言中是一种常见的代码缺陷，内存泄漏的检测方法目前主要是静态分析与动态检测.动态检测开销大，且高度依赖测试用例；静态分析目前被学术界和工业界广泛应用，但是存在大量误报，需要人工对检测结果进行确认.内存泄漏静态分析的误报通常是由于对指针、分支语句和全局变量分析的不准确性导致的.提出了一种内存泄漏的智能化检测方法，通过使用机器学习算法学习程序特征与内存泄漏之间的相关性，构建机器学习分类器，并应用机器学习分类器进一步提高内存泄漏静态分析的准确性.首先构建机器学习分类器，然后通过静态分析方法构建从内存分配点开始的Sparse Value Flow Graph（SVFG），并从中提取内存泄漏相关特征，再使用规则和机器学习分类器进行内存泄漏的检测.实验结果显示，该方法在分析指针、分支语句和全局变量时是有效的，能够提高内存泄漏检测的准确性，降低内存泄漏检测结果的误报.最后，对未来研究的可行性以及面临的挑战进行了展望. |
| 关键词: 内存泄漏 内存泄漏检测 静态分析 机器学习 特征提取 |
| DOI：10.13328/j.cnki.jos.005715 |
| 分类号: |
| 基金项目:国家重点研发计划（2016YFB1000802）；国家自然科学基金（61802168，61632015）；中央高校基本科研业务费专项资金（020214380047） |
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| Memory Leak Intelligent Detection Method for C Programs |
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ZHU Ya-Wei, ZUO Zhi-Qiang, WANG Lin-Zhang, LI Xuan-Dong
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State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210023, China
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| Abstract: |
| Memory leak is a common code bug for C programs which uses explicit memory management mechanisms. At present, the main detection methods of memory leaks are static analysis and dynamic detection. Dynamic detection has huge overhead and it is highly dependent on test cases. Static analysis is widely used by academic and industry, but there are a large number of false positives, which need to be manually confirmed. Inaccuracy in the analysis of pointers, branch statements, and global variables leads to false positives in static analysis of memory leaks. In this study, an intelligent detection method is proposed for memory leak. By using machine learning algorithms to learn the correlation between program's features and memory leaks, a machine learning classifier is built and applied to improve the accuracy of static analysis of memory leaks. Firstly, a machine learning classifier is trained. Then, the sparse value flow graph (SVFG) starting from allocation should be constructed by using the static analysis, the features related to memory leaks can be extracted from the SVFG. Lastly, the memory leaks are detected by using rules and machine learning classifier. Experimental results show that the proposed method is effective in analyzing pointers, branch statements, and global variables, and can reduce the false positives of memory leak detection. At the end of this paper, the feasibility of future research and the upcoming challenges are presented. |
| Key words: memory leak memory leak detection static analysis machine learning extracting feature |
