LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB
Large Language Models (LLMs) have achieved impressive results on public benchmarks, often leading to claims of advanced reasoning and understanding. However, recent research in cognitive science reveals that these models sometimes rely on shallow heuristics and memorization, taking shortcuts rather than demonstrating genuine cognitive abilities. This paper investigates LLM behavior in automated test generation for software, contrasting performance on an open-source system (LevelDB) with SAP HANA, one of the most widely deployed commercial database systems worldwide, whose proprietary codebase is guaranteed to be absent from training data. We combine cognitive evaluation principles, drawin...
| Authors | Vekil Bekmyradov, Noah C. Pütz, Thomas Bartz-Beielstein |
|---|---|
| Primary category | cs.SE |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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