Inferring rich input structure for software debugging and defence

软件系统无处不在，对现代生活至关重要，但同时它们也往往是黑客的复杂、容易出错和高价值的目标。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大价值。改进的测试和调试工具可以降低软件开发的成本并提高生产率。这些更低的成本和更高的生产率对于维持加拿大不断增长的软件行业非常重要。这些相同的技术还可以提高软件的可靠性，提供更好的保证，确保计算机程序做它们想要做的事情，并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等关键软件公司的主要关注点。现有的自动化调试和测试方法受到用于指导分析的有效输入的模型质量的限制。它们主要集中在考虑输入的句法有效性。相反，该建议侧重于为程序自动构建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。

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.