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
- Generate a basic corpus of labeled data from a different set of project related with openSUSE
- Evaluate the best features to make a proper classification: n-gram, PoS tag, TF-IDF (with and without stemmer)
- Evaluate and measure the best classification model: Naive Bayes, Linear SVM, Max Entropy, ...
This project is part of:
Hack Week 10 Hack Week 11 Hack Week 12
Jobs in openQA are us...
This project is plann...
Avahi Integration and Network Connection
Collect flaky test cases identified by the team in a GitHub board and highlight them in the Test report by oscar-barrios
Flaky tests: Th...