Use machine learning and natural language processing techniques to analyze the changes made in a project, and classify them in:

  • Small / unimportant fix
  • Big / important fix
  • Small / important feature
  • Big / important feature

For this project I will

  1. Generate a basic corpus of labeled data from a different set of project related with openSUSE
  2. Evaluate the best features to make a proper classification: n-gram, PoS tag, TF-IDF (with and without stemmer)
  3. Evaluate and measure the best classification model: Naive Bayes, Linear SVM, Max Entropy, ...

Looking for mad skills in:

nlp machinelearning git github

This project is part of:

Hack Week 10 Hack Week 11 Hack Week 12

Activity

  • about 2 years ago: jordimassaguerpla liked Detect type of change in a project analyzing the log history
  • over 2 years ago: nicolasbock liked Detect type of change in a project analyzing the log history
  • almost 4 years ago: ZRen disliked Detect type of change in a project analyzing the log history
  • almost 4 years ago: ZRen liked Detect type of change in a project analyzing the log history
  • almost 4 years ago: bkutil liked Detect type of change in a project analyzing the log history
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    Comments

    • aplanas
      over 4 years ago by aplanas | Reply

      Yeah. Hackweek 10 collied with openSUSE 13.1, so I will try to for on this during this new Hackweek instance : )

    • froh
      over 4 years ago by froh | Reply

      Would it be hard to train for regression fix vs new feature, based on the comment? I'd be curious how much energy project have to put into regression fixes vs feature additions.

    • osynge
      almost 4 years ago by osynge | Reply

      Have you considered looking at ELK and integrating this work in the ELK stack.

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