Bugs classification using machine learning

I've made a prototype using TensorFlow

Automatic bug classification - predicting the module with NPP network using torch._ _

comment: # Acheaving accuracy of 90% and loss under 35%

Achieving accuracy of 90% and loss under 35%

Resources

comment: # keywords: ML machine learning DNN natural language processing AI comment: # Some clean up of this site and PR. Interest? comment: # keywords: ML machine learning DNN natural language processing AI

https://github.com/rtsvet/SLE-Bugs/tree/main/Text_Classifier

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This project is part of:

Hack Week 22

Activity

  • over 2 years ago: rtsvetkov liked this project.
  • over 2 years ago: rtsvetkov originated this project.

  • Comments

    • aylarose495
      4 days ago by aylarose495 | Reply

      Automatic bug classification using Machine Learning (ML), Natural Language Processing (NLP), and Deep Neural Networks (DNN) is revolutionizing software maintenance. By automating the categorization and prioritization of bugs, teams can resolve issues faster and more efficiently. NLP helps understand bug reports written in natural language, while DNNs enhance accuracy by learning complex patterns from large datasets.

      This kind of intelligent automation not only improves productivity but also supports scalability in modern development pipelines—much like how innovations in consumer tech, such as the best vaping are streamlining user experience through high-capacity, reliable performance.

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