Sakura is a multi-agent system that generates structurally complex tests from NL descriptions, achieving 50-78% higher compilability and 38-66% higher coverage overlap than baselines on 1,464 scenarios from 20 Apache Commons applications.
Generating high-level test cases from requirements using LLM: An industry study,
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A clustering-based pipeline generates individual and integration-level test specifications from thousands of automotive requirements by grouping embeddings, summarizing clusters, and applying LLM calls with bounded context and standards grounding.
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Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions
Sakura is a multi-agent system that generates structurally complex tests from NL descriptions, achieving 50-78% higher compilability and 38-66% higher coverage overlap than baselines on 1,464 scenarios from 20 Apache Commons applications.
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Cluster-Aware Dual-Level Test Specification Generation for Large-Scale Automotive Software Requirements
A clustering-based pipeline generates individual and integration-level test specifications from thousands of automotive requirements by grouping embeddings, summarizing clusters, and applying LLM calls with bounded context and standards grounding.