SHF: Medium: Collaborative Research: Testing in the Era of Approximation

许多计算，如图像处理、机器学习和工程模拟，本质上都是近似的--它们为了更好的性能而牺牲了结果的质量。然而，近似也引入了新的挑战时，推理程序的行为和发现错误。目前，这方面的测试需要更有原则和有效的方法。同时，近似本身为测试领域的创新提供了一个有效的新基础，从而使测试更加有效和有价值。该项目将开发测试和自动逼近的双向集成，开发和优化日益重要的一类程序的新方法。结果将纳入开放源码工具集，并使用开放源码和专有应用程序进行严格评估。该项目将为编译程序、程序分析和软件工程等课程开发新的教材和课程材料，具体而言，将开发一套测试近似程序的技术和工具，包括测试规范语言和将现有测试自动迁移到新语言的技术、动态近似程序分析技术和最佳近似发现技术。 此外，该项目将开发近似计算技术，以提高回归测试和突变测试的性能。

Many computations, such as image processing, machine learning, and engineering simulations are inherently approximate -- they trade off quality of results for better performance. However, approximation also introduces new challenges when reasoning about program behaviors and finding bugs. At present, testing in this area requires more principled and effective approaches. Simultaneously, approximation itself provides an effective new basis for innovations in the well-trodden field of testing, thereby making testing more efficient and valuable. The project will develop a bi-directional integration of testing and automated approximation, new approach for developing and optimizing an increasingly important class of programs. The results will be embodied in open source tool sets and rigorously evaluated using open-source and proprietary applications. New educational and course materials will be developed for courses on compilers, program analysis and software engineering.More concretely, the project will develop a set of techniques and tools for testing approximate programs, including a test specification language and techniques for automated migration of existing tests to the new language, techniques for dynamic approximate-program analysis, and techniques for optimal approximation discovery. Moreover, the project will develop approximate computing techniques to improve the performance of regression testing and mutation testing.