High-level decision diagram manipulations for code coverage analysis

Abstract

Previous works have shown that high-level decision diagrams (HLDD-s) are suitable for system representation for analyzing code coverage metrics. This is due to the fact that HLDD models implicitly represent classical code coverage items, such as statement and branch coverage. However, research on the properties of HLDD-s, which contribute to the accuracy of coverage assessment, is missing. Current paper proposes a set of HLDD manipulations in order to generate diagrams that would allow more stringent code coverage measurement without sacrificing performance, i.e., computation time and memory requirements. The techniques include generation of HLDD-trees from Hardware Description Language (HDL)descriptions and two types of HLDD collapsing methods, which are a generalization of the BDD reduction rules. Experiments on ITC99 benchmark circuits show that the code coverage assessment based on the proposed HLDD manipulation is more stringent than what can be achieved with classical methods. At the same time, the model is well scalable because HLDD generation is terminated in the HDL variables.