Collaborative Research: SHF: Small: Towards Variability-Aware Software Analysis and Testing

社会所依赖的许多软件都是高度可配置的，具有许多可选和可选择的功能，可以打开或关闭。一些选项组合在一起“发挥作用”。选项的其他组合可能导致软件崩溃或行为不正确，必须避免。在对安全至关重要的应用程序（例如在医疗或航空航天领域）中，错误的各种功能可能是危险的。随着软件变得越来越复杂，并提供更多的特性，在需求规范和代码中实际实现的约束之间不允许的组合之间，断开连接的风险增加了。与最先进的技术相比，该项目将能够对软件产品线和可配置系统进行更有效的分析和测试。它将训练学生进行越来越自动化的软件分析，以验证复杂的、高度可配置的软件系统和产品线。该项目旨在扩展软件分析技术，以自动从程序代码中提取功能约束，并根据软件需求中的功能约束检查它们。目标是帮助自动修复任何不一致，并派生出对可变性约束实现高覆盖率的测试。该项目将通过调整程序分析技术（如符号执行和静态分析）来利用可变性感知软件分析，从而在中间表示级别实现可变性感知。可变性约束将使用可变性感知分析从软件中自动提取。这将能够评估可变性对功能性和非功能性特性的影响。此外，提取的可变性约束将用于检查软件是否满足可变性需求，并确定任何所需的修复。该项目将在现实世界的系统中应用和评估所提出的方法，特别关注安全关键约束，并开发出一系列挑战问题，这些问题反映了开发人员在实践中面临的困难，供研究人员和课程使用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Much of the software upon which society depends is highly configurable, with many optional and alternative features that can be turned on or off. Some combinations of options “play well” together. Other combinations of options may cause the software to crash or behave incorrectly and must be avoided. A wrong variety of features can be dangerous in safety-critical applications, such as in the health or aerospace domains. As software becomes more complex and offers more features, the risk of disconnects increases between those combinations that are disallowed per the requirements specifications and those constraints that are actually implemented in the code. The project will enable more effective analysis and testing of software product lines and configurable systems compared to the state-of-the-art. It will train students in the increasingly automated software analysis needed to verify complex, highly configurable software systems and product lines.This project aims to extend software analysis techniques to automatically extract feature constraints from the program’s code and check them against the feature constraints in the software requirements. The goal is to help automatically repair any inconsistencies and to derive tests that achieve high coverage of the variability constraints. The project will leverage variability-aware software analysis by adapting program analysis techniques such as symbolic execution and static analysis to be variability-aware at the intermediate-representation level. Variability constraints will be automatically extracted from software using variability-aware analysis. This will enable evaluating the impact of variability on the functional as well as non-functional properties. Additionally, extracted variability constraints will be used to check whether the software meets variability requirements and identify any required repairs. The project will apply and evaluate the proposed approach to real-world systems, paying particular attention to safety-critical constraints, and develop a set of challenge problems that reflect difficulties that developers face in practice for use by researchers and in coursework.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.