Test Case Generation from Bug Reports via Large Language Models: A Cognitive Layered Evaluation Framework

• URL: http://arxiv.org/abs/2510.05365v1
• Date: Mon, 06 Oct 2025 20:47:12 GMT
• Title: Test Case Generation from Bug Reports via Large Language Models: A Cognitive Layered Evaluation Framework
• Authors: Irtaza Sajid Qureshi, Zhen Ming, Jiang,
• Abstract summary: We present a systematic evaluation of Large Language Models (LLMs) reasoning in test case generation.<n>We evaluate StarCoder and GPT-4o on Defects4J, GHRB, and mutated variants that introduce linguistic and semantic challenges.
• Score: 10.919459368597295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly applied to automated software testing, yet their ability to generalize beyond memorized patterns and reason about natural language bug reports remains unclear. We present a systematic evaluation of LLM reasoning in test case generation, structured around the cognitive layers of Bloom's taxonomy: \textit{Remember}, \textit{Understand}, \textit{Apply}, \textit{Analyze}, \textit{Evaluate}, and \textit{Create}, which progressively assess higher levels of cognitive and reasoning capabilities. Building on the LIBRO framework, we evaluate StarCoder and GPT-4o on Defects4J, GHRB, and mutated variants that introduce linguistic and semantic challenges. Our findings show that both models largely reproduce prior results with minor deviations (\textit{Remember}), exhibit partial robustness to linguistic rephrasings and translations while uncovering unique reproducible bugs (\textit{Understand}), but suffer severe performance drops exceeding 60\% under identifier mutations (\textit{Apply}). Conversely, providing near-identical few-shot examples in an open-book setting improves success rates by up to three times, and component-level analysis reveals that structured technical elements, such as test code and method names, are far more impactful than narrative descriptions for successful test generation (\textit{Analyze}). These insights illuminate the cognitive processes underlying LLM-generated tests, suggest concrete directions for improving performance, and establish a robust and realistic evaluation paradigm for this task.

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