Evaluating LLM-Based Test Generation Under Software Evolution
Large Language Models (LLMs) are increasingly used for automated unit test generation. However, it remains unclear whether these tests reflect genuine reasoning about program behavior or simply reproduce superficial patterns learned during training. If the latter dominates, LLM-generated tests may exhibit weaknesses such as reduced coverage, missed regressions, and undetected faults. Understanding how LLMs generate tests and how those tests respond to code evolution is therefore essential. We present a large-scale empirical study of LLM-based test generation under program changes. Using an automated mutation-driven framework, we analyze how generated tests react to semantic-altering chang...
| Authors | Sabaat Haroon, Mohammad Taha Khan, Muhammad Ali Gulzar |
|---|---|
| Primary category | cs.SE |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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