Programming Online Verification Exam (OE) system has been widely used in algorithm education and practice since it can automatically analyze program results (e.g., correct or incorrect) after executing the submitted programs with corresponding test cases. OE systems can provide both execution results and error information so that novice programmers can get feedback quickly. If the submitted program cannot pass all the test cases, the novice programmers will get wrong-answer feedback, and they have to find and fix the errors in the program. Automated program fault localization techniques, which can locate the errors in programs under test automatically, thus help novice programmers fix the errors quickly. However, the performance of current automated fault localization techniques is limited due to the high-density test cases in novice programs of OE system. In this paper, we analyze the impact of test case density (TCD) and execution variety on fault localization performance and propose a method to reduce TCD to improve fault localization precision for novice programs. To evaluate the performance of our method, we conduct a number of empirical studies on 1199 real fault diagnosis algorithm related novice programs, and the experimental results show that using improved test cases through our method for fault localization in OE system can enhance the precision of fault localization for novice programs. Specifically, after decreasing the test cases’ density, the improvement of fault localization accuracy ranges from 0.6% to 17.34% in terms of the Expense metric, and from the [email protected] metrics, the number of faulty statements that can be found increases in most cases.

编程在线验证考试（OE）系统在算法教育和实践中得到了广泛的应用，因为它可以在执行提交的程序和相应的测试用例后自动分析程序结果（例如，正确或不正确）。OE系统可以同时提供执行结果和错误信息，让新手程序员可以快速得到反馈。如果提交的程序不能通过所有的测试用例，新手程序员会得到错误答案反馈，他们必须找到并修复程序中的错误。自动化程序故障定位技术，可以自动定位被测程序中的错误，帮助新手程序员快速修复错误。然而，由于OE系统的新手程序中的高密度测试用例，当前的自动故障定位技术的性能受到限制。在本文中，我们分析了测试用例密度 (TCD) 和执行多样性对故障定位性能的影响，并提出了一种减少 TCD 以提高新手程序故障定位精度的方法。为了评估我们方法的性能，我们对 1199 个真实故障诊断算法相关的新手程序进行了大量的实证研究，实验结果表明，通过我们的方法在 OE 系统中使用改进的测试用例进行故障定位可以提高故障的精度新手程序的本地化。具体来说，在降低测试用例密度后，故障定位精度的提高范围为 0.6% 至 17%。