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) 第二阶段项目的更广泛/商业影响使人工智能 (AI) 无需编码即可自动执行测试，从而降低了软件质量保证 (SQA) 端到端测试的成本和速度或经验丰富的编码员。 随着创新型高增长软件即服务 (SaaS) 公司更快地进入市场、更有信心、软件缺陷更少，行业将通过节省时间和金钱而获得经济利益。该 SBIR 第二阶段项目将构建一个人工智能解决方案，尽管该解决方案由经验不足的软件工程师使用，但将允许软件公司以最少的用户交互来识别软件缺陷。实时引导流程直接从 Web 浏览器收集信息，处理测试自动化的传统和未解决的问题，例如软件测试设计、自动化、覆盖率和维护。该人工智能解决方案将通过执行两项主要任务来提高 SQA 的效率：模拟实时用户对 Web 应用程序的探索和识别意外行为。该架构使人工智能代理能够自学习并与应用程序交互，从而改进每次观察。人工智能学习周期在系统内实现彻底的通信，因为它根据自己的知识分析所产生的效果来传达应用特定行动的请求。第一阶段的研究证明，该架构可以升级到商业版本，为希望提高产品软件质量并更快进入市场的客户提供价值。第二阶段的预期技术成果将增强对意外软件行为的分类，优化数据分析时间，并缩短学习周期。该奖项反映了 NSF 的法定使命，通过使用基金会的智力优势和更广泛的评估，被认为值得支持。影响审查标准。