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An enhanced approach for automatic annotation of error codes based on Seq2edit.

Jian Wang1,2, Tao Lin1, Rongsen Zhao1

  • 1College of Computer Science, Sichuan University, Chengdu, Sichuan, China.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

This study improves automatic code error correction by enhancing the Seq2edit annotation method. By considering source code sentence structure, it boosts annotation accuracy for better deep learning models.

Keywords:
Annotation accuracyAutomatic annotationCode alignmentDeep learningLevenshtein algorithmSeq2edit

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Deep natural language translation models show promise for automatic code error correction.
  • High-quality, large-scale annotated datasets are crucial for model performance.
  • Current Seq2edit-based annotation methods face challenges with token alignment accuracy.

Purpose of the Study:

  • To propose an improved Seq2edit annotation method for code errors.
  • To enhance the accuracy of automatic code error annotation.
  • To optimize training datasets for code error correction models.

Main Methods:

  • Developed a Seq2edit annotation method incorporating source code sentence structure.
  • Divided code into statements with independent meanings.
  • Introduced a cost coefficient to refine the Levenshtein algorithm for edit distance calculation and token alignment.

Main Results:

  • The proposed method effectively utilizes source code contextual information during annotation.
  • Demonstrated a significant improvement in the accuracy of automatic code error annotation.
  • Enhanced token alignment capabilities by optimizing edit distance calculations.

Conclusions:

  • The sentence-structure-based Seq2edit method significantly improves automatic code error annotation accuracy.
  • This advancement facilitates the creation of larger, more accurate datasets for code error correction.
  • The approach holds potential for advancing deep learning models in software development.