# Rule-Based Error Classification for Analyzing Differences in Frequent Errors

arXiv: https://arxiv.org/abs/2311.00513 · HTML: https://mlsanity.com/paper/2311.00513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [A Survey on Online Judge Systems and Their Applications](https://mlsanity.com/paper/1710.05913/) (2017)
- [Deduplicating and Ranking Solution Programs for Suggesting Reference Solutions](https://mlsanity.com/paper/2307.07940/) (2023)

## Abstract (arXiv)

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.