An Extensible Perceptron Framework For Revision Rtl Debug Automation

Automated debugging techniques can significantly reduce the manual effort required to localize RTL errors. These techniques return to the user a set of RTL locations where a change can correct erroneous behavior. However, each location must be manually investigated. This problem is exacerbated by the increasing amount of failures in the modern regression verification cycle. Recent work in clustering-based revision debugging mitigates this cost by ranking revisions based on their likelihood of having introduced an error. This work presents a perceptron-based approach to revision debugging that can be extended to leverage the revision history of a design directly. Perceptrons are trained using labeled revisions from the design history. They are then used to predict the probability that a revision has introduced an error. The proposed methodology performs competitively with the state-of-the-art, but can be extended to handle more features. This allows for an automated regression debug flow integrated with Version Control and Issue Tracking Systems.