The Impact of Generative AI on Test & Evaluation: Challenges and Opportunities
| dc.contributor.author | Freeman, Laura | en |
| dc.contributor.author | Robert, John | en |
| dc.contributor.author | Wojton, Heather | en |
| dc.date.accessioned | 2025-08-12T16:56:01Z | en |
| dc.date.available | 2025-08-12T16:56:01Z | en |
| dc.date.issued | 2025-06-23 | en |
| dc.date.updated | 2025-08-01T07:49:32Z | en |
| dc.description.abstract | Generative Artificial Intelligence (GenAI) is transforming software development processes, including test and evaluation (T&E). From automating test case design to enabling continuous testing in DevOps pipelines, AI-driven tools enhance the efficiency, accu-racy, and speed of software testing. At the same time, the integra-tion of AI components into software-reliant systems introduces new challenges for verification and validation (V&V). Traditional T&E methodologies must evolve to address issues such as AI bias, hal-lucinated outputs, and the complexity of validating non-determin-istic behaviors. This position paper examines how existing T&E methods must evolve to account for AI’s stochastic nature, and con-versely how GenAI is transforming T&E practices across the soft-ware development lifecycle (SDLC). | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1145/3696630.3728723 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/137470 | en |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | The author(s) | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | The Impact of Generative AI on Test & Evaluation: Challenges and Opportunities | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |