Directed Testing

Technique

Directed testing is a hardware and digital-system verification technique in which 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 substantial manual effort and scaling challenges as design complexity grows. In modern verification flows it is typically combined with constrained-random testing, property checking, reference-model step-and-compare flows, and benchmark-driven validation; coverage-directed selection, LLM-assisted generation, Python-UVM co-verification, and module-directed extensions build on or refine the core idea.

First seen 6/14/2026

Last seen 9/30/2026

Evidence 16 chunks

Wiki v4

WIKI

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]. In the processor verification literature, directed testing is characterized as an engineer-driven approach in which testers carefully craft specific scenarios to validate known functionalities and verify that features work as intended [chunk:3115804d-7529-488e-abc6-2a6eca425b72].

Definition and characteristics

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NEIGHBORHOOD

1 nodes · 0 edges

graph · Directed Testing · depth=1

RELATIONSHIPS

14 connections

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

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

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.

toggle coverage uses → 100% 2e

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

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.

FLAME ← mentions 85% 1e

Directed testing is presented as a traditional dynamic verification method.

riscv-tests ← implements 70% 1e

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

Directed Test Generation for Hardware Validation: A Survey (ACM CS 2023) ← evaluates 70% 1e

As a survey of directed test generation, it evaluates methods and approaches in directed testing.

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.

white-box testing uses → 100% 1e

Directed testing uses white box approach to target coverage holes.

CITATIONS

11 sources

11 citations — click to expand

[1] Directed testing is one of the traditional workhorses of digital-system verification and is precise and targeted but requires significant manual effort. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[2] FLAME characterizes directed testing as engineer-driven and observes it becomes costly and insufficient for scenarios at the periphery of expected behaviors as designs grow. MoDitector: Module-Directed Testing for Autonomous Driving Systems

[3] The LFI directed testbench workflow uses a black-box test specification reviewed by design and verification teams and VHDL BFMs driving read/write/idle transactions with a separate protocol checker. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[4] Reported LFI coverage figures: 100% justified statement coverage, 98.4% branch, 95.2% condition, 84.2% trigger, 100% toggle for directed tests; bug-type breakdown shows property checking found all 11 bus-protocol violations, directed testing found all 5 livelock/deadlock bugs, and random testing found 22 bugs across categories. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[5] NaxRiscv verification flow combines directed testing with real-world benchmarking and constrained-random testing, beginning with riscv-tests and riscv-arch-tests in a Jenkins CI pipeline. Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

[6] ConfigDrivenV reports that verification engineers allocate more than 70% of project time to RTL verification and characterizes existing approaches as either SystemVerilog-internal randomization or manually written directed tests with limited design-space coverage. ConfigDrivenV: A Python-Integrated UVM Testbench for Automated RTL Verification with Self-Checking Scoreboard

[7] ConfigDrivenV validates a Python golden reference model plus UVM self-checking scoreboard on a 16-bit RISC processor and a UART, reporting 100% verification accuracy on correct RTL and 100% bug detection on corrupted RTL. ConfigDrivenV: A Python-Integrated UVM Testbench for Automated RTL Verification with Self-Checking Scoreboard

[8] Imperas OVP step-and-compare flow encapsulates an Instruction Set Simulator (OVP model) into a SystemVerilog module via DPI, loads the same .elf into DUT and OVP memories, and steps both targets in lockstep while comparing dynamic state and accumulating functional coverage into results.log. Imperas verification RISC-V Global Forum 2020 v3

[9] MoDitector introduces module-specific oracles, module-specific feedback, adaptive seed selection, and mutation to generate tests that provoke failures in specific ADS modules (perception, prediction, planning, control), extending directed testing to module-level fault attribution. MoDitector: Module-Directed Testing for Autonomous Driving Systems

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

[11] Jayasena and Mishra, 'Directed Test Generation for Hardware Validation: A Survey', ACM Computing Surveys, vol. 56, no. 5, pp. 136, Dec. 2023, is cited as a systematic survey of directed test generation techniques for hardware validation. ConfigDrivenV: A Python-Integrated UVM Testbench for Automated RTL Verification with Self-Checking Scoreboard