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Finding and fixing errors is a time-consuming task not only for noviceprogrammers but also for expert programmers. Prior work has identified frequenterror patterns among various levels of programmers. However, the differences inthe tendencies between novices and experts have yet to be revealed. From theknowledge of the frequent errors in each level of programmers, instructors willbe able to provide helpful advice for each level of learners. In this paper, wepropose a rule-based error classification tool to classify errors in code pairsconsisting of wrong and correct programs. We classify errors for 95,631 codepairs and identify 3.47 errors on average, which are submitted by variouslevels of programmers on an online judge system. The classified errors are usedto analyze the differences in frequent errors between novice and expertprogrammers. The analyzed results show that, as for the same introductoryproblems, errors made by novices are due to the lack of knowledge inprogramming, and the mistakes are considered an essential part of the learningprocess. On the other hand, errors made by experts are due to misunderstandingscaused by the carelessness of reading problems or the challenges of solvingproblems differently than usual. The proposed tool can be used to createerror-labeled datasets and for further code-related educational research.

Rule-Based Error Classification for Analyzing Differences in Frequent Errors