Differential coverage: : automating coverage analysis

While it is easy to automate coverage data collection, it is a time consuming/difficult/expensive manual process to analyze the data so that it can be acted upon. The goal of the approaches discussed in here is to reduce the cost and barrier to entry of using coverage data analysis in large-scale projects by categorizing and prioritizing coverage changes to avoid the need for manual review at every release or on every build.Differential coverage and date binning are methods of combining coverage data and project/file history to determine if goals have been met and to identify areas of unexercised code which should be reviewed. These methods can be applied to any coverage metric which can be associated with a location - statement, function, expression, toggle, etc. - and to any language, including both software (C++, Python, etc.) and hardware description languages (SystemVerilog, VHDL).The approach is realized in diffcov
¹
, a recently released open-source tool.