Block-wise Test Case Generation

Overview

Block-wise test case generation is a structured approach to constructing test inputs for hardware fuzzing. Rather than relying on random mutation of full instruction sequences or ad-hoc refinement, it assembles test cases by iteratively selecting and concatenating pre-defined instruction blocks — coherent units of one or more instructions — into a complete test program. The selection process is guided to balance inter-instruction qualities (how blocks interact when composed) and intra-instruction qualities (the internal characteristics of each block).

Motivation

Existing hardware fuzzers have historically suffered from limited semantic awareness and inefficient test refinement. They rely heavily on random mutations or heuristic strategies that fail to capture the complex dependencies and execution semantics inherent to modern Instruction Set Architectures (ISAs). This produces duplicated test cases and leaves edge cases unexplored. Block-wise test case generation addresses this gap by imposing a compositional structure on input synthesis: each test is built up from reusable, semantically meaningful blocks, allowing the fuzzer to reason about coverage and vulnerability signals at the block level rather than only at the level of raw byte sequences.

How It Works

During a fuzzing pipeline that employs block-wise generation, the fuzzer:

Maintains a pool of instruction blocks. Each block encapsulates one or more instructions with a known internal structure.
Iteratively constructs a test case by concatenating carefully chosen blocks from this pool.
Balances inter- and intra-instruction quality, meaning the choice of which block to append considers both how the new block interacts with previously selected blocks and the standalone quality of the block itself.
Leverages feedback — typically coverage signals and, where available, vulnerability indicators — to influence which blocks are selected in subsequent iterations.

By partially decoupling test case construction from full test execution, block-wise generation enables faster, lower-cost refinement of inputs. This is particularly valuable in hardware settings where each test execution against a Device Under Test (DUT) can be expensive due to slow simulation.

Adoption

Block-wise test case generation is implemented by GoldenFuzz, a two-stage hardware fuzzing framework that uses a fast, ISA-compliant Golden Reference Model (GRM) as a "digital twin" of the DUT. GoldenFuzz uses block-wise generation to construct tests that are first refined against the GRM and then replayed against the DUT, accelerating deep architectural exploration while keeping computational overhead low.

Advantages

Compared with random or purely heuristic test generation, block-wise generation:

Encodes ISA semantics into the generation process, reducing duplication.
Allows feedback signals to be attributed to specific blocks, supporting more targeted refinement.
Yields shorter test cases that achieve higher coverage, lowering simulation cost per unit of coverage.
Is compatible with a two-stage pipeline in which a fast reference model filters and improves tests before expensive DUT evaluation.