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Detecting non-natural language artifacts for de-noising bug reports.

Thomas Hirsch1, Birgit Hofer1

  • 1Institute of Software Technology, Graz University of Technology, Inffeldgasse 16b, 8010 Graz, Austria.

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|September 6, 2022
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Summary
This summary is machine-generated.

This study introduces a machine learning method to automatically remove non-natural language artifacts from software engineering text, improving natural language processing (NLP) and information retrieval (IR) accuracy.

Keywords:
Artifact removalBug reportsData cleaningDe-noisingIssue ticketsNLP

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Area of Science:

  • Software Engineering
  • Natural Language Processing (NLP)
  • Machine Learning (ML)

Background:

  • Software engineering documents like issue tickets contain non-natural language artifacts (code, logs).
  • These artifacts pose challenges for NLP and information retrieval (IR) methods, necessitating noise reduction.
  • Effective preprocessing is crucial for accurate analysis of software engineering text.

Purpose of the Study:

  • To develop a machine learning approach for classifying lines in textual documents as either natural language or non-natural language artifacts.
  • To automate the generation of training data from GitHub issue trackers and project documentation.
  • To create a Markdown-agnostic model capable of artifact removal in un-annotated content.

Main Methods:

  • Utilized GitHub issue trackers and Markdown-annotated files for automated training set generation.
  • Developed a custom preprocessing technique for artifact removal.
  • Trained a Markdown-agnostic machine learning model to classify content at a line level.
  • Evaluated the model on issue tickets from C++, Java, JavaScript, PHP, and Python projects.

Main Results:

  • Achieved high ROC-AUC scores ranging from 0.92 to 0.96 for language-specific models.
  • A multi-language model demonstrated strong performance with ROC-AUC scores between 0.92 and 0.95.
  • The approach effectively distinguishes natural language from non-natural language artifacts.

Conclusions:

  • The developed machine learning models serve as effective noise reduction preprocessing steps for NLP and IR on software engineering issue tickets.
  • Automated training data generation significantly enhances the practicality and scalability of artifact removal.
  • The models offer a robust solution for cleaning textual data in software engineering contexts.