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

SHF：媒介：协作研究：近似时代的测试

• 批准号: 1704790
• 负责人: Milos Gligoric
• 金额: $ 45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704790&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Testing

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.

许多计算（例如图像处理，机器学习和工程模拟）本质上是近似近似的 - 它们可以将结果质量取得更高的质量。但是，在推理程序行为并查找错误时，近似也会引入新的挑战。目前，在这一领域进行测试需要更多有效的有效方法。同时，近似本身为在良好的测试领域中创新提供了有效的新基础，从而使测试更加有效和有价值。该项目将建立测试和自动近似的双向整合，开发和优化日益重要的程序类别的新方法。结果将体现在开源工具集中，并使用开源和专有应用进行严格评估。将开发新的教育和课程材料，用于编译器，程序分析和软件工程课程。更具体地说，该项目将开发一套用于测试近似程序的技术和工具，包括测试规范语言和技术，用于自动化现有测试的技术，以对新语言进行自动化，以进行新的语言，以进行动态分析的技术，以进行动态分析，以实现最佳的近似技术，并发现。 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。

项目成果

Testing Probabilsitic Programming Systems

测试概率规划系统

• 发表时间: 2018
• 期刊: International Symposium on Foundations of Software Engineering
• 作者: Dutta, Saikat;Legunsen, Owolabi;Huang, Zixin;Misailovic, Sasa
• 通讯作者: Misailovic, Sasa

PSense: Automatic Sensitivity Analysis for Probabilistic Programs

• DOI: 10.1007/978-3-030-01090-4_23
• 发表时间: 2018-10
• 期刊: Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages
• 作者: Zixin Huang;Zhenbang Wang;Sasa Misailovic

Regression test selection for TizenRT

TizenRT 的回归测试选择

• DOI: 10.1145/3236024.3275527
• 发表时间: 2018
• 期刊: Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Celik, Ahmet;Lee, Young Chul;Gligoric, Milos
• 通讯作者: Gligoric, Milos

Mutation Analysis for Coq

• DOI: 10.1109/ase.2019.00057
• 发表时间: 2019-11
• 期刊: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Ahmet Çelik;Karl Palmskog;Marinela Parovic;E. J. G. Arias;Miloš Gligorić

Approximate Transformations as Mutation Operators

• DOI: 10.1109/icst.2018.00036
• 发表时间: 2018-04
• 期刊: 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST)
• 作者: Farah Hariri;A. Shi;Owolabi Legunsen;Miloš Gligorić;S. Khurshid;Sasa Misailovic