Reinforcement learning-based directed test generation

Technique

A functional-verification technique that formulates directed test generation as a reinforcement-learning (RL) problem, in which an agent learns a policy for constructing or extending test programs so as to drive coverage toward pre-specified verification targets. The approach was introduced by Pfeifer, Zimpel, Andrade et al. (DATE 2020), who applied it to directed test generation for shared-memory verification.

First seen 6/25/2026

Last seen 6/25/2026

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Definition

Reinforcement learning-based directed test generation is a directed test generation technique for hardware / pre-silicon verification in which the search for stimuli that hit a pre-selected verification target is cast as a sequential decision-making problem and solved with reinforcement learning. Instead of relying on unconstrained or constraint-random biasing (refs. [5], [10], [17]) or on Bayesian-network / coverage-directed heuristics (ref. [11]), an RL agent iteratively selects actions that extend or mutate a test program and is rewarded according to how much closer the resulting stimulus brings the verification campaign to its goal.

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A reinforcement learning approach to directed test generation for shared memory verification ← uses 90% 1e

Applies a reinforcement learning-based approach to directed test generation.

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CITATIONS

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[1] Reinforcement learning-based directed test generation was introduced by Pfeifer, Zimpel, Andrade et al. at DATE 2020 in their paper 'A reinforcement learning approach to directed test generation for shared memory verification' (pp. 538–543). A RISC-V Test Sequences Generation Method Based on Instruction ... (reference list, ref. [12])

[2] The technique is a form of directed test generation, i.e. stimulus generation aimed at pre-specified verification targets, as opposed to unconstrained or purely random stimulus generation. A RISC-V Test Sequences Generation Method Based on Instruction ... (reference list, refs. [5], [10], [11], [17])

[3] RL-based directed test generation is closely related to coverage-directed test generation using Bayesian networks (Braun, Fine and Ziv, ref. [11]) and to CSP-based random test program generation (Bin et al., ref. [17]; Ayadi et al., ref. [15]). A RISC-V Test Sequences Generation Method Based on Instruction ... (reference list, refs. [11], [15], [17])

[4] The technique is situated in the broader context of instruction-set / RISC-V verification, where directed generation is used to reach corner-case instruction sequences (refs. [3], [7], [8], [13], [14]). A RISC-V Test Sequences Generation Method Based on Instruction ... (reference list, refs. [3], [7], [8], [13], [14])