Directed Testing

Technique

Directed testing is a hardware and digital-system verification technique in which verification engineers hand-craft specific test cases to exercise particular functionalities or corner cases of a design, rather than relying on randomly generated stimuli. It is one of the traditional 'workhorse' methodologies of digital design verification, valued for being precise and targeted but limited by the substantial manual effort required to anticipate issues and by scaling challenges as design complexity grows. In modern multi-layer verification flows it is typically combined with constrained-random testing and benchmark-driven validation, and is supported by compliance suites such as riscv-tests and riscv-arch-tests for RISC-V cores.

First seen 6/14/2026

Last seen 7/18/2026

Evidence 12 chunks

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Overview

Directed testing is a verification methodology in which engineers deliberately construct specific test cases to validate targeted functionalities, corner cases, or microarchitectural scenarios of a design under test (DUT). It is described as one of the traditional 'workhorses' of digital-system verification, alongside constrained-random testing, and is characterized as precise and targeted but requiring significant manual effort to author comprehensive test suites [chunk:2525ccc3-a666-4b13-84b8-d765c09f7803].

Definition and characteristics

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NEIGHBORHOOD

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RELATIONSHIPS

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toggle coverage uses → 100% 2e

Directed testing targeted 100% toggle coverage as a structural coverage metric.

Bus Functional Model (BFM) uses → 100% 2e

Directed testing used VHDL Bus Functional Models for the test bench.

VHDL uses → 100% 2e

Directed testing used VHDL for writing Bus Functional Models.

Black Box Test Specification uses → 100% 2e

Directed testing started with writing a black box test specification document.

structural coverage uses → 100% 2e

Directed testing targets structural coverage metrics such as statement, branch, toggle coverage.

Protocol Checker uses → 100% 2e

The directed test bench included protocol checkers to monitor bus adherence.

white-box testing uses → 100% 1e

Directed testing uses white box approach to target coverage holes.

riscv-arch-tests ← implements 70% 1e

The methodology executes riscv-arch-tests in the directed testing stage to check ISA conformance.

RISC-V UVM Verification Thesis ← uses 96% 1e

The thesis includes directed tests in its verification infrastructure.

System-Level Verification of Multi-Core RISC-V ← uses 95% 1e

The paper employs directed tests in addition to random testing.

A Comparison of Three Verification Techniques: Directed Testing, Pseudo-Random Testing and Property Checking ← evaluates 100% 1e

The paper compares and evaluates directed testing as one of three verification techniques.

riscv-tests ← implements 70% 1e

As part of the multi-level strategy, the initial phase of directed testing executes the riscv-tests compliance suite.

LINKED ENTITIES

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riscv-tests IMPLEMENTS Extracted graph relationship

riscv-arch-tests IMPLEMENTS Extracted graph relationship

CITATIONS

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7 citations — click to expand

[1] Directed testing involves creating specific test cases to verify particular functionalities or corner cases of a design; it is precise and targeted but requires significant manual effort. Reinforcement Learning Framework for RISC-V Functional Verification

[2] The NaxRiscv verification methodology is structured as a multi-layered approach that combines directed testing, real-world benchmarking, and constrained-random testing, beginning with execution of the riscv-tests and riscv-arch-tests compliance suites in a Jenkins CI pipeline. Comprehensive lockstep verification for NaxRiscv SoC integrating a superscalar core supporting multiple ISA extensions

[3] Directed and constrained-random testing are described as the traditional 'workhorses' of digital system verification, with constrained-random testing potentially missing rare corner cases and being inefficient in test count, motivating machine-learning-based alternatives. Reinforcement Learning Framework for RISC-V Functional Verification

[4] RISC-V's modular approach with many optional extensions and custom instructions multiplies the number of configurations that must be verified, making purely manual directed-testing approaches harder to scale. Reinforcement Learning Framework for RISC-V Functional Verification

[5] Module-directed testing (e.g., MoDitector) extends directed-testing ideas to module-level fault attribution in autonomous driving systems, using module-specific oracles, feedback, adaptive seed selection, and mutation to provoke module-specific failures. MoDitector: Module-Directed Testing for Autonomous Driving Systems

[6] Coverage-directed test selection uses supervised learning from coverage feedback to bias test selection toward tests likely to increase functional coverage, reducing manual constraint writing and accelerating coverage closure in simulation-based verification. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[7] An RL-driven framework for RISC-V functional verification uses coverage metrics and CPU state (register file, program counter) to guide automatic assembly-code generation, reporting a coverage improvement over random instruction generation. Reinforcement Learning Framework for RISC-V Functional Verification