WekaNose is a tool that allows to perform an experiment, that aims to study code smell detection through machine learning techniques. The experiment's purpose is to select rules, or obtain trained algorithms, that can classify an instance (method or class) as affected or not by a code smell. These rules have the main advantage that they are extracted through an example-based approach, rather then a heuristic-based one. This experiment is divided in two main part:
- the first one concern the creation of the dataset
- the second part where the machine learning algorithms are trained and tested using the dataset created in the first part.
For every further information about this tool:
- check out the WekaNoseTutorial.pdf
- visit the Website
- read the related paper published at ICSE '18: doi, paper
- check out the presentation (link to slideshare)
For report any problem please check if already exist an Issue on GitHub about it:
- if there isn't one please add an Issue
- if there is one please leave a comment
It is now available a SonarQube plugin that allows to use the machine learning algorithms, provided by WEKA and trained using the dataset generated by WekaNose, as actual code smell detector and it is available here.
The next steps in this project are:
- integrating Auto-WEKA in order to make the training of the machine learning algorithms more user-friendly;
- add a new feature that allows the user to exploit semi-supervised learning (such as active learning) to speed-up the labelling process;
If you are interested in contributing to this project in any way (suggestion of improvement, hint on what feature to add or even pull request) do not hesitate to contact me at u.azadi@campus.unimib.it.