Rule-Based Error Classification for Analyzing Differences in Frequent
  Errors
Atsushi Shirafuji Taku Matsumoto Md Faizul Ibne Amin Yutaka Watanobe
Finding and fixing errors is a time-consuming task not only for novice
programmers but also for expert programmers. Prior work has identified frequent
error patterns among various levels of programmers. However, the differences in
the tendencies between novices and experts have yet to be revealed. From the
knowledge of the frequent errors in each level of programmers, instructors will
be able to provide helpful advice for each level of learners. In this paper, we
propose a rule-based error classification tool to classify errors in code pairs
consisting of wrong and correct programs. We classify errors for 95,631 code
pairs and identify 3.47 errors on average, which are submitted by various
levels of programmers on an online judge system. The classified errors are used
to analyze the differences in frequent errors between novice and expert
programmers. The analyzed results show that, as for the same introductory
problems, errors made by novices are due to the lack of knowledge in
programming, and the mistakes are considered an essential part of the learning
process. On the other hand, errors made by experts are due to misunderstandings
caused by the carelessness of reading problems or the challenges of solving
problems differently than usual. The proposed tool can be used to create
error-labeled datasets and for further code-related educational research.