SHF: Small:Automatically Synthesizing System and Integration Tests

系统和集成测试是软件开发和维护中的一组关键活动，用于确定应用程序及其组成组件是否正常运行。系统/集成测试对于确保软件质量至关重要，因为在不同组件之间的交互中会出现许多严重的软件缺陷。不幸的是，创建有效的系统/集成测试需要大量的时间和精力，因为测试来自重要软件应用程序的组件的所有组合的巨大集合是不可行的。通过系统/集成测试有效地发现故障，并有效地消耗资源和执行时间，对于降低软件成本和提高软件质量是至关重要的。总而言之，系统/集成测试中有一些以前没有考虑到的独特因素，如果不加以解决，这些因素将继续限制软件应用程序的质量。在这个项目中，将创建一个新的框架，用于自动创建有效的系统/集成测试，从而高效地找到错误。将为系统和集成软件测试的自动综合(ASSIST)开发一个三管齐下的研究计划。将创建和评估新的算法和技术，作为该项目工作的一部分，以自动获得描述相互作用组件的模型，从而减少综合系统和集成测试的数量，并提高其故障查找能力。此外，还将创建一种新的方法，其中使用静态和动态分析以及机器学习来获得测试输入数据以及综合系统和集成测试的预言。作为该项目工作的结果，系统和集成测试将使用具有与手动创建的集成测试类似的错误查找能力的辅助来自动合成。一套新的辅助工具，将被开发、评估并应用于不同的开源和商业应用程序，并向更广泛的社区提供。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

System and integration testing is a set of key activities in software development and maintenance to determine if applications and their constituent components behave correctly. System/integration testing is vitally important for ensuring software quality, since many serious software defects occur in interactions among different components. Unfortunately, creating effective system/integration tests requires significant time and effort, since it is not feasible to test the enormous set of all combinations of the components from a nontrivial software application. It is crucial to reduce the cost and increase the quality of software with system/integration testing that is effective in fault finding and efficient in resource consumption and execution time. In sum, there are unique factors in system/integration testing, not previously considered, that if not addressed will continue to limit the quality of software applications. In this project, a novel framework will be created for automatically creating effective system/integration tests that find bugs efficiently. A three-pronged research program will be developed for Automatic Synthesis of System and Integration Software Tests (ASSIST). New algorithms and techniques will be created and evaluated as part of the work on this project for automatically obtaining models that describe interacting components, thus reducing the number of synthesized system and integration tests and increasing their fault-finding power. Also, a novel way will be created in which static and dynamic analyses and machine learning are used to obtain test input data as well as oracles for the synthesized system and integration tests. As a result of the work on this project, system and integration tests will be automatically synthesized using ASSIST that have a comparable bug-finding power with manually created integration tests. A suite of new ASSIST tools that will be developed, evaluated, and applied to different open-source and commercial applications, and made available to the broader community.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.