Sparse Demand-Driven Analysis to Improve Software Reliability and Security

稀疏需求驱动分析以提高软件可靠性和安全性

• 批准号: DP150102109
• 负责人: Prof Jingling Xue
• 金额: $ 18.37万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-03-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP150102109
• 关键词: Sparse; Demand; Driven; Analysis; Improve

Current static analysis tools can eliminate many bugs missed by traditional testing but they are still imprecise or inefficient. This project aims to develop precise pointer analyses that enable -finding clients to detect bugs efficiently in large-scale programs in C/C++ and Java, where pointers are used pervasively. The novelty lies in performing these analyses sparsely (allowing data-flow information to move directly from variable definitions to their potential uses) based on Context-Free-Language-reachability (enabling client queries to be answered on-demand). The outcomes aim to significantly improve the reliability and security of industrial-sized software.

目前的静态分析工具可以消除传统测试遗漏的许多错误，但它们仍然不精确或效率低下。该项目旨在开发精确的指针分析，使查找客户端能够有效地检测C/C++和Java中大规模程序中的错误，其中指针被广泛使用。新颖之处在于稀疏地执行这些分析（允许数据流信息直接从变量定义移动到它们的潜在用途），基于上下文无关可达性（使客户端查询能够按需回答）。这些成果旨在显著提高工业规模软件的可靠性和安全性。