论文标题
差分覆盖范围：自动覆盖范围分析
Differential coverage: automating coverage analysis
论文作者
论文摘要
虽然很容易自动化覆盖范围数据收集，但分析数据是一个耗时/困难/昂贵的手动过程，以便可以对其进行采取。复杂性来自众多来源，其中未经测试或测试不佳的旧版代码和第三方库是最常见的两个。 差异覆盖范围和日期套件是结合覆盖范围数据和项目/文件历史记录的方法，以确定是否已达到目标并确定应优先考虑的代码领域。这些方法可以应用于可以与位置相关联的任何覆盖范围指标 - 语句，功能，表达式，切换等 - 以及任何语言，包括软件（C ++，Python等）和硬件说明语言（Systemverilog，vhdl）。这些方法的目的是减少在大规模项目中使用覆盖范围数据分析的成本和障碍。 该方法在最近发布的开源工具Gendiffcov中实现。
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. Complexity arises from numerous sources, of which untested or poorly tested legacy code and third-party libraries are two of the most common. 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 code which should be prioritized. 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 goal of these approaches is to reduce the cost and the barrier to entry of using coverage data analysis in large-scale projects. The approach is realized in gendiffcov, a recently released open-source tool.
