Abstract: A method for generating Huffman codewords to encode a dataset includes selecting a Huffman tree type from a plurality of different Huffman tree types. Each of the Huffman tree types specifies a different range of codeword length in a Huffman tree. A Huffman tree of the selected type is produced by: determining a number of nodes available to be allocated as leaves in each level of the Huffman tree accounting for allocation of leaves in each level of the Huffman tree; allocating nodes to be leaves such that the number of nodes allocated in a given level of the Huffman tree is constrained to be no more than the number of nodes available to be allocated in the given level; and assigning the leaves to symbols of the dataset based an assignment strategy selected from a plurality of assignment strategies to produce symbol codeword information.

Abstract: Aspects of the disclosure provide methods and systems for improving test generation using constraint solving problem (CSP) techniques. A test method can include modeling a circuit as logic constraints to correlate outputs of the circuit as logic functions of inputs of the circuit, pre-determining at least a value constraint that specifies a desired output value for an output of the circuit, and solving input values for the inputs to satisfy the logic constraints for the circuit and the value constraint of the output.