LLM-Powered Test Case Generation for Detecting Tricky Bugs

• URL: http://arxiv.org/abs/2404.10304v1
• Date: Tue, 16 Apr 2024 06:20:06 GMT
• Title: LLM-Powered Test Case Generation for Detecting Tricky Bugs
• Authors: Kaibo Liu, Yiyang Liu, Zhenpeng Chen, Jie M. Zhang, Yudong Han, Yun Ma, Ge Li, Gang Huang,
• Abstract summary: AID generates test inputs and oracles targeting plausibly correct programs. We evaluate AID on two large-scale datasets with tricky bugs: TrickyBugs and EvalPlus. The evaluation results show that the recall, precision, and F1 score of AID outperform the state-of-the-art by up to 1.80x, 2.65x, and 1.66x, respectively.
• Score: 30.82169191775785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs. Large Language Models (LLMs) can be prompted to produce test inputs and oracles for a program directly, but the precision of the tests can be very low for complex scenarios (only 6.3% based on our experiments). To fill this gap, this paper proposes AID, which combines LLMs with differential testing to generate fault-revealing test inputs and oracles targeting plausibly correct programs (i.e., programs that have passed all the existing tests). In particular, AID selects test inputs that yield diverse outputs on a set of program variants generated by LLMs, then constructs the test oracle based on the outputs. We evaluate AID on two large-scale datasets with tricky bugs: TrickyBugs and EvalPlus, and compare it with three state-of-the-art baselines. The evaluation results show that the recall, precision, and F1 score of AID outperform the state-of-the-art by up to 1.80x, 2.65x, and 1.66x, respectively.

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