SBIR Phase II: Artificial Intelligence Powered Software Testing

SBIR 第二阶段：人工智能驱动的软件测试

• 批准号: 2223011
• 负责人: Ivan Barajas Vargas
• 金额: $ 97.18万
• 依托单位: MUUKLABS INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223011&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Artificial; Intelligence

The broader/commercial impact of the Small Business Innovation Research (SBIR) Phase II project reduce the cost and speed of software quality assurance (SQA) end-to-end testing by enabling artificial intelligence (AI) to automate tests without the need for coding or highly experienced coders. As innovative high-growth Software as a Service (SaaS) companies go to market faster with more confidence and fewer software defects, industries will benefit economically by saving time and money. This SBIR Phase II project will build an AI solution which, although used by less experienced software engineers, will allow software companies to identify software defects with minimal user interactions. The real-time and guided process gathers information directly from the web browsers, handling traditional and unresolved problems with test automation such as software test design, automation, coverage, and maintenance. The AI solution will make SQA highly efficient by performing two major tasks: simulating real-time users' exploration of web applications and identifying unexpected behaviors. The architecture enables AI agents to self-learn and interact with the application, improving on each observation. The AI learning cycle implements thorough communication within the system as it communicates requests to apply specific actions based on its own knowledge analyzing the resulting effect. Phase I research proved that the architecture can be upgraded to a commercial version, providing value to customers looking to improve software quality in their products and go to market faster. The anticipated technical results in Phase II will enhance the categorization of unexpected software behaviors, optimize the data analysis time, and reduce the learning cycle.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.

小型企业创新研究（SBIR）II期项目的更广泛/商业影响降低了软件质量保证的成本和速度，通过启用人工智能（AI）来自动化测试而无需编码或经验丰富的编码器来自动化测试，从而降低了端到端测试的成本和速度。 随着创新的高增长软件作为服务（SaaS）公司以更快的信心和更少的软件缺陷的速度更快地推销市场，行业将通过节省时间和金钱在经济上受益。这个SBIR II期项目将构建一个AI解决方案，尽管经验不足的软件工程师使用，但它将允许软件公司识别具有最小用户交互的软件缺陷。实时和有指导的过程直接从Web浏览器中收集信息，以通过软件测试设计，自动化，覆盖范围和维护来处理传统和未解决的问题。 AI解决方案将通过执行两个主要任务来使SQA高效：模拟实时用户对Web应用程序的探索并识别意外行为。该体系结构使AI代理能够自学习并与应用相互作用，从而改善每个观察结果。 AI学习周期在系统内实现彻底的通信，因为它可以根据自己的知识来分析所得效果，以应用特定的操作。第一阶段的研究证明，该体系结构可以升级到商业版本，为希望改善产品中的软件质量并更快地上市的客户提供价值。第二阶段的预期技术结果将增强意外软件行为的分类，优化数据分析时间并减少学习周期。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来审查标准的评估值得支持的。