Inferring rich input structure for software debugging and defence

推断丰富的输入结构用于软件调试和防御

• 批准号: RGPIN-2020-06394
• 负责人: Sumner, William
• 金额: $ 1.75万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741183
• 关键词: Inferring; rich; input; structure; software

Software systems are pervasive and critical to modern life, yet at the same time they are often complex, error prone, and high valued targets for hackers. As a result techniques that can help improve the quality of software can provide significant value to both industry and society. Improved tools for testing and debugging can lower the costs and increase productivity within software development. These lower costs and increased productivity are important for maintaining Canada's growing software industry. These same techniques can also improve the reliability of software, providing better assurances that computer programs do what they are intended to and are more resilient to malicious users. Improving the reliability and cost-effectiveness of software development has been a primary focus over the last decade for key software companies like Google, Apple, Microsoft, Facebook, and IBM. Existing state of the art approaches for automating debugging and testing are limited by the quality of the models for valid inputs that they use to guide analyses. They focus primarily on considering the syntactic validity of inputs. This proposal instead focuses on automatically constructing models of semantically valid inputs for a program. These models can be produced without human interaction and help enable the automated construction of customized tools for testing and debugging software.

软件系统在现代生活中无处不在且至关重要，但同时它们往往是复杂的，容易出错的，并且是黑客的高价值目标。因此，有助于提高软件质量的技术可以为行业和社会提供重要价值。改进的测试和调试工具可以降低成本并提高软件开发的生产力。这些降低的成本和提高的生产力对于维持加拿大不断增长的软件产业非常重要。这些相同的技术还可以提高软件的可靠性，更好地保证计算机程序做它们想要做的事情，并且对恶意用户更具弹性。 在过去的十年里，提高软件开发的可靠性和成本效益一直是谷歌、苹果、微软、Facebook和IBM等主要软件公司的主要关注点。用于自动化调试和测试的现有技术水平的方法受限于它们用于指导分析的有效输入的模型的质量。他们主要关注输入的句法有效性。相反，该建议侧重于自动构建程序的语义有效输入的模型。这些模型可以在没有人工交互的情况下生成，并有助于自动构建用于测试和调试软件的定制工具。