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SBIR Phase II: Artificial Intelligence Powered Software Testing

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2223011
Agency Tracking Number: 2223011
Amount: $971,804.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AI
Solicitation Number: NSF 22-552
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-05-01
Award End Date (Contract End Date): 2025-04-30
Small Business Information
400 w north st APT1500 APT1500
Raleigh, NC 27603
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Ivan Barajas Vargas
 (919) 407-3193
Business Contact
 Ivan Barajas Vargas
Phone: (919) 407-3193
Research Institution

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. _x000D_
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._x000D_
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.

* Information listed above is at the time of submission. *

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