Modern verification is a highly automated process that involves many tools and subsystems. These verification tools produce a large amount of data that is essential for understanding the state and progress of the verification process. The complexity of the verification, the amount of data it produces, and the complex relations between the data sources demands sophisticated data science techniques. These include statistics, data visualization, data mining, and machine learning to extract the essence of the input data and present it to the users in a simple and clear manner.
The Verification Cockpit (VC) provides the platform and means to collect, process, and analyze verification data. The data is primarily collected from the project’s version control (git, svn), test submission and failure tracking system (hdwb), bug tracking tool (ClearQuest), coverage (BugSpray) and planning (RTC).