SBIR Phase I: MuukTest Artificial Intelligence Powered Software Testing

SBIR 第一阶段：MuukTest 人工智能驱动的软件测试

• 批准号: 2016187
• 负责人: Ivan Barajas Vargas
• 金额: $ 25.6万
• 依托单位: MUUKLABS INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016187&HistoricalAwards=false
• 关键词: SBIR; Phase; MuukTest; Artificial; Intelligence

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will enable non-technical users to create complete and comprehensive software test automation. Additionally, it will enable growing software companies to ship their products faster with higher quality at a reasonable cost. Currently, these companies spend up to 50% of their resources on quality assurance (QA) and testing. This project will develop an automated process for testing software.This Small Business Innovation Research (SBIR) Phase I project will combine symbolic reasoning algorithms, deep learning, and reinforcement learning models to automate software quality assurance testing. The two problems being addressed by this project are (1) software development teams spend up to 50% of their time testing software, and (2) they have to hire skilled software engineers to automate these tests. The objective of this research is to use artificial intelligence (AI) to make software quality assurance testing faster and enable non-technical workers to create sophisticated tests without the need to code. The proposed research aims to create an AI prototype by (i) building a baseline of manual test scenarios, (ii) building tests using symbolic reasoning (SR), (iii) incorporating deep learning, and (iv) adding reinforcement learning to generate tests.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.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力将使非技术用户能够创建完整且全面的软件测试自动化。此外，它将使成长中的软件公司能够以合理的成本更快地交付更高质量的产品。目前，这些公司将高达 50% 的资源用于质量保证 (QA) 和测试。该项目将开发用于测试软件的自动化流程。该小型企业创新研究 (SBIR) 第一阶段项目将结合符号推理算法、深度学习和强化学习模型来自动化软件质量保证测试。该项目要解决的两个问题是（1）软件开发团队花费高达 50% 的时间来测试软件，（2）他们必须聘请熟练的软件工程师来自动化这些测试。这项研究的目标是利用人工智能 (AI) 加快软件质量保证测试速度，并使非技术人员无需编写代码即可创建复杂的测试。拟议的研究旨在通过（i）构建手动测试场景的基线，（ii）使用符号推理（SR）构建测试，（iii）结合深度学习，以及（iv）添加强化学习来生成测试来创建人工智能原型该奖项反映了 NSF 的法定使命，并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，被认为值得支持。