Machine Learning

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Revision as of 20:11, 8 March 2018 by Lji (talk | contribs) (Machine Learning FAQ)
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Machine Learning Overview

Machine Learning prediction is a Globalyzer workbench and Globalyzer Lite feature that help users handle false positive issues. We suggest applying machine learning as a follow-up step to scanning with Rule Sets. It helps to determine which candidate issues using Rule Sets are indeed i18n issues.


Prerequisite: Python 3.6.x and 3.x

1. Download Python version 3.6+ from website

2. Install python and add python to PATH environment variable

3. Go to this link and make sure you navigate to the "INSTALL IN PYTHON" tab as shown below.

 Install dependencies (prepending with `sudo` if needed):
 pip install requests
 pip install tabulate
 pip install scikit-learn
 pip install colorama
 pip install future

At the command line, copy and paste these commands one line at a time:

 pip uninstall h2o
 pip install

Success if response messages have "Successfully installed h2o-"

Test1: Open System Command and type in "python -V", success if reply python version like "Python 3.6.2"

Test2: On the command line, go into python. In python:

> import h2o
> h2o.init()

This should complete without errors.

Work Flow

Firstly, you need to create a globalyzer project with scans in Globalyzer client. At the Scan Results view, you could right mouse click on the issue that you determine it's a false positive issue, and choose "Mark prediction as false positive(F)" from the menu. Please at least marking several issues as false positives before applying "Find more false positives" under Machine Learning menu.

After marking some issues as false positives, please click "Find more false positives" button under "Machine Learning" menu, and wait the predicting process finish. There are three possible predictions "ML False", "ML NULL" and "ML True". Based on your marked issues and filtered issues by the rule set, machine learning will predict some issues as "ML False". "ML NULL" means machine learning can't predict this issue is an indeed issue or false positive, this is because the input data is not enough to let machine learning have a prediction for this issue.

If you find issues be predicted as "ML False" are indeed issues, you could right mouse click on the issue and select "Mark prediction as true positive(T)", and in next time you run "Find more false positives" machine learning will learn your correction. And if you are not satisfied with the prediction results, please continue marking more issues as "F" or "T", and rerun "Find more false positives".

Machine Learning FAQ

1. If I change issues status, will machine learning work?

Yes, it will work. when you change issues status, the prediction of the issue will be changed by default. For example, if you move issues to "Todo" status, the prediction will be marked as True, if you move issues to "Ignore"/"Invalid" status, the prediction will be marked as False. However, you could still mark a "Todo" issue as "False" manually.

2. How does machine learning work?

We use to analyze the issue, the issue code line and the issue reason. Based on filtered issues and your marked false issues, machine learning will try to find similar issues with them and change the prediction as "ML False" for those similar issues. So machine learning prediction will be different per project per scan, you won't have an exactly same result every time, machine learning just gives the prediction. In addition, machine learning needs a category as input to learn, which means if you only mark one issue as "False Positives", there are high possibly that machine learning cannot find other issues similar with it.