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，具有许多可选和替代功能，可以打开或关闭。一些选项的组合在一起“玩得很好”。其他选项组合可能会导致软件崩溃或行为不正确，必须避免。在安全关键型应用中，例如在健康或航空航天领域中，错误的特征种类可能是危险的。随着软件变得越来越复杂并提供更多的功能，在需求规范不允许的那些组合和代码中实际实现的那些约束之间断开的风险增加。该项目将使软件产品线和可配置系统的更有效的分析和测试相比，国家的最先进的。它将培训学生在日益自动化的软件分析需要验证复杂的，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，并根据功能约束检查它们在软件需求中。其目标是帮助自动修复任何不一致性，并派生出实现可变性约束的高覆盖率的测试。该项目将通过调整程序分析技术（如符号执行和静态分析）来利用可变性感知的软件分析，以在中间表示层实现可变性感知。将使用可变性感知分析从软件中自动提取可变性约束。这将能够评价可变性对功能和非功能特性的影响。此外，提取的可变性约束将用于检查软件是否满足可变性要求并识别任何所需的修复。该项目将应用和评估所提出的方法，以现实世界的系统，特别注意安全关键的约束，并制定一套挑战性的问题，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NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.