Machine Learning

Machine Learning is a powerful feature available from the Globalyzer Workbench, and subsequently, Globalyzer Lite, that helps users more quickly identify the real issues in their source code. Note that Machine Learning requires installation of third party software. Please see information on our wiki.

Globalyzer Workbench

To use Machine Learning, first create a Globalyzer project with scans in the Globalyzer Workbench. At the Scan Results view, right mouse click on some issues that you determine are not real issues, and choose Mark prediction: FALSE(F) from the menu. Please mark the prediction of several issues as false before applying Machine Learning.

After marking the prediction of several issues as false, please select Machine Learning->GO!, and wait for the predicting process to finish. Possible prediction values for each active issue are:

• -: Issue has not been marked by a user and Machine Learning hasn't been invoked yet
• T: Marked by a user (or detected by rule) as a real issue to train Machine Learning
• F: Marked by a user as a false issue to train Machine Learning
• ML True: Machine learning prediction that the issue is a true issue, i.e. the issue should be refactored
• ML False: Machine Learning prediction that the issue is a false issue and can be ignored
• ML NULL: Machine Learning cannot make a prediction, so must be considered a true issue

Note that filtered issues are predicted as Negative and used to train Machine Learning.

If you find that issues predicted as ML False are indeed issues, please right mouse click on the issue and select Mark prediction: TRUE(T); the next time you run Go, Machine Learning will learn your correction. If you are not satisfied with the prediction results, please continue marking more issues as F or T, and rerun Machine Learning.

Once you are satisfied with the prediction results, the issues with a prediction value of T, ML True, or ML NULL are the true issues that need to be addressed. The issues with a prediction value of F or ML False can be ignored. The suggested way to view the predicted active issues is to select Scan Views->All Predicted Active.

Globalyzer Lite

To use Machine Learning when running Globalyzer Lite, you need to first run the Workbench and invoke Machine Learning as described above on the desired scans in the project. Then, when you export the project to Lite, choose to export Machine Learning for those scans. This generates special Machine Learning files in the project/lingoport directory, and sets flags in the generated project definition file (PDF) to use Machine Learning.

When Lite runs a PDF with scans using Machine Learning, it first scans the source using rules in the rule sets, and then invokes Machine Learning processing. The generated XML report will include Machine Learning prediction information, which will be read by the Dashboard. The Dashboard will only display active issues with a prediction value of T, ML True, or ML NULL. Active issues with a prediction value of F or ML False will not be displayed on the Dashboard.

Continuous Integration System

When using Machine Learning in our Continuous Integration System, make sure everything in the project/lingoport directory is pushed to your repository. This ensures that everything downstream will work as intended.