Partial Coverage Task Completion via Functional Model Deduction
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Solution Overview
Problem
Partial coverage tasks in functional coverage analysis are incomplete due to unassigned functional attributes, leading to reduced accuracy in test planning and coverage analysis, as existing tests may not provide sufficient information for value assignments.
Innovation Solution
Enhance partial coverage tasks by using the functional coverage model to deduce valid values for unassigned attributes based on model restrictions, employing methods like Binary Decision Diagrams (BDDs) and Boolean Satisfiability Problem (SAT) solving to determine singleton values or valid assignments, ensuring accurate representation of the test space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If partial coverage tasks are used in functional coverage analysis, then testing efficiency is improved by utilizing existing tests, but accuracy deteriorates due to unassigned functional attributes
Solution Approach 1:
The system performs preliminary action by using the functional coverage model to pre-determine and assign values to unassigned functional attributes before coverage analysis is completed. This allows existing tests to be fully utilized while maintaining accuracy by filling in missing information proactively rather than reactively.
Solution Approach 2:
The functional coverage model acts as an intermediary between the partial coverage tasks and the complete test specifications. It mediates by providing the logical constraints and relationships needed to infer missing attribute values, thus bridging the gap between incomplete test data and accurate coverage measurement.
2Measurement precision
If functional coverage model restrictions are applied to deduce attribute values, then accuracy of coverage analysis is improved, but device complexity increases
Solution Approach 1:
The functional coverage model performs self-service by automatically inferring and assigning values to unassigned functional attributes using its own internal restrictions and logical constraints. This eliminates the need for external manual intervention or complex external systems to provide the missing information.
Solution Approach 2:
The system changes the state of unassigned functional attributes from unknown to determined by applying logical deductions based on model restrictions. This transformation of parameters from incomplete to complete states enables accurate coverage analysis without requiring overly complex external systems.
Data Source
AI summary
A method, apparatus and product for completion of partial coverage tasks. The method comprising obtaining a partial coverage task defining a test with respect to a functional coverage model of a System Under Test (SUT), wherein the functional coverage model defining functional attributes and respective domains thereof, wherein the functional coverage model further defining one or more restrictions on value combinations of the functional attributes; and enhancing the partial coverage task to include an assignment of a value to a functional attribute, wherein the functional attribute is unassigned in the partial coverage task, wherein the value is an only valid assignment to the functional attribute in view of assignments of other functional attributes and in view of the restrictions.