Abstract: In recent years,large language model(LLM) have been widely applied to test case generation and prioritization.However,existing studies show that LLM-generated test cases still suffer from high error rates and semantic deviations.Empirical evaluations indicate that only about 40% of the generated code is directly executable,with roughly one-third of the failures caused by assertion logic errors.These issues significantly undermine the effectiveness and reliability of LLM-based fault diagnosis.To address this problem,this paper proposes a fault diagnosis-oriented test case generation and prioritization method based on LLM.Firstly,the proposed approach extracts code summaries from the original program using an LLM to generate substitute programs that implement equivalent functionality.Then,the LLM is employed to produce initial test cases,whose expected outputs are refined through multiple substitute programs.Next,the differences between the outputs of the original and substitute programs are analyzed,and test cases are prioritized based on the function call relationships of the original program.Finally,program faults are diagnosed efficiently according to the prioritized sequence.Experiments are conducted on seven benchmark problems comprising 140 Python programs.The results demonstrate that the proposed method significantly reduces the error rate of LLM-generated test cases.The resulting test sequences achieve high fault coverage across multiple LLM,with an average fault detection rate(APFD) consistently above 0.75,confirming the proposed method’s effectiveness in improving test accuracy and fault diagnosis efficiency.

Key words: Large language model, Program fault detection, Test case generation, Test case prioritization