Diving Into the Components of ML-Based Regression Failure Analysis and Debug

By Synopsys, Inc.

November 17, 2022

Recently we wrote about how AI-driven debug automation technology can accelerate the root-cause analysis of regression failures. In that blog we introduced the Synopsys Verdi Regression Debug Automation (RDA) technology that helped customers like MediaTek achieve a 4X improvement in identifying root-causes of failures in their design. This blog will take a deeper look into the components of this RDA technology, explain how they work and how users can take advantage to achieve similar results.

First, let’s recap what Synopsys Verdi RDA does from a high-level. Each time a regression fails, teams must often examine 100’s if not 1,000s of failures and debug their causes. Synopsys Verdi RDA uses machine learning (ML) to automate the process of finding the root causes of failures in the design under test and testbench. The technology automatically classifies and probes these raw regression failures. The failures are then automatically triaged to identify if they are part of the design or the testbench. Design and verification teams then perform root-cause analysis to pinpoint the bug(s) triggering these failures.

Now let’s take a closer look at the components that automate and accelerate regression debug. The process starts by collecting data from the regression run. The collected data feeds into the Regression Binning application which then analyzes the log files and classifies these failures into different buckets according to error types such as UVM-based messaging, user-defined rules, verification IP, and instruction set errors. The results can then be visualized in Synopsys Verdi where users can filter and search the results and then invoke interactive debug. The process has been shown to be 90% accurate and reduces overall triage time.

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