At MTVCon, Dec 10-11 2018, Austin, TX Christian Graber from Verifyter presented a paper on bug prediction using machine learning called "Boosting Continuous Integration performance with Machine Learning".

This presentation was part of a session focused on how machine learning can be used in conjunction with mining version control data to predict bugs.

As part of this session Alper Sen, associate professor from Bogazici University, presented a paper called "Predicting Buggy Modules During Virtual Prototype Developement" which focused on predicting bugs in SystemC with very good results.

Avi Ziv from IBM Research presented "Mining version control data - are software and hardware the same?" where he talked about what the hardware community can learn from the software community, especially in the field called MSR, Mining Software Repositories, which is a very active software research community centered around the annual MSR conference. He also described some concrete work that have been done in this area inside IBM.

At DVCon Europe, Oct 24-25, Munich we presented "Enabling Visual Design Verification Analytics – From Prototype Visualizations to an Analytics Tool using the Unity Game Engine" where we showed how the bug reports generated by PinDown, our automatic debugger for regression tests, can be visualized in a cool way that enables an analytical view. It allows you to see which areas of the design that are error-prone and need some extra attention. It also allows you to identify areas of the design that lack test coverage. Here is a demo. We have explored this field earlier this year, but now we went more into the filtering aspects of the visualizer. You can set the time frame and search for a certain activity level/fault ratio to make it easier to analyze the data.

 

 

Most of the time we spent in the booth talking to verification people about how PinDown uses Machine Learning to predict bugs before verification even starts. This speeds up PinDown's own debug process and it also allow you to run the regression runs from a risk point of view (large test suite for risky commits, small test suites for safe commits).

 

 

At DAC55, June 25-27 at Moscone Center West, San Francisco, we announced that the latest release of PinDown is out now. This release uses machine learning to automatically debug regression failures much faster. Also, the bug prediction is available to users immediately, even before the verification starts. This means that you can direct the verification and debug efforts towards the riskier commits instead of using the standard brute force approach of trying to run as much verification as possible on everything. 

 

 

 

As expected with a hot topic like machine learning this new release generated a lot of interest at our booth at DAC, more than we have ever seen before.