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TestBrain Risk Model

TestBrain’s machine learning analyzes your commit history to determine the risk of defect in each code change.

Analyzes Historical Commit and Defect Data

To build a model of the factors contributing to code defects, TestBrain’s machine learning reviews the code development history using:

  • The git blame feature of git-based repositories to find bugs and bug fixes applied over the entire development history.
  • Optional integration with Jira or other defect management tool to import a database of defects and the commits that caused them.
  • On-going learning once TestBrain is installed from visibility into new test failures.

Machine Learning Builds a Defect Risk Model

TestBrain looks for patterns in the commits which led to defects in the past and uses these patterns to determine the probability of defects in new commits. A few of the factors the model considers include:

  • commit details: size of commit, number of files changed, number of files and areas affected
  • area of code changed: some areas of the code area more defect prone than others, or may be stale code
  • purpose of commit: whether the commit is adding new code, updating legacy code, or fixing a bug
  • developer habits: whether the code was submitted at unusual times or before a deadline; how much experience the developer has with this area of the code 
  • other activity: how many other developers are working on the same code area  


Want More Technical Details?

Read the TestBrain machine learning white paper that explains the TestBrain risk model in detail.

Making Your Testing Smarter
With Machine Learning

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