AI-based failure aggregation

Modern automated testing environments generate vast amounts of test results, making failure analysis increasingly complex as both the number of tests and failures grow. This presentation introduces an AI-driven approach to failure aggregation, leveraging text embeddings and semantic similarity to efficiently group and analyze unique failures. The workflow integrates open-source, pre-trained models for text embedding (such as Sentence Transformers) and vector similarity search using PostgreSQL with pgvector, enabling scalable and low-barrier adoption.