coverage-guided test generation

Coverage-guided test generation

Concept

Coverage-guided test generation is a family of testing techniques that steer the production of tests or instruction streams using coverage-related feedback. The provided evidence documents its application in three domains: (1) RISC-V processor verification, where a randomized instruction-stream generator evolves at runtime based on observed coverage in tight co-simulation with an ISS, combined with Coverage-guided Aging to regularize the coverage distribution; (2) LLM-driven software testing, where a code-aware prompting strategy (SymPrompt) decomposes test generation into execution-path-aligned stages; and (3) deep learning system testing, where combinatorial-coverage criteria are adapted into a CT coverage-guided test generation technique.

First seen 5/26/2026

Last seen 6/5/2026

Evidence 7 chunks

Wiki v2

WIKI

Overview

Coverage-guided test generation is a class of testing techniques that use coverage-related feedback to steer the production of tests or instruction streams. The evidence covers three distinct application domains: RISC-V processor verification, LLM-driven software test generation, and deep learning system testing.

RISC-V processor verification

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RELATIONSHIPS

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Instruction Injection ← implements 90% 2e

Instruction injection is used to inject instructions that cover specific coverage points, implementing coverage-guided test generation.

Coverage-guided Aging ← extends 90% 2e

Coverage-guided Aging extends coverage-guided test generation by smoothing the coverage distribution over time.

Bayesian network-based test generation part of → 85% 1e

Bayesian network-based test generation is a form of coverage-guided test generation.

LINKED ENTITIES

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Instruction Injection IMPLEMENTS Extracted graph relationship

Coverage-guided Aging EXTENDS Extracted graph relationship

CITATIONS

8 sources

8 citations — click to expand

[1] A randomized coverage-guided instruction stream generator produces an endless, unrestricted instruction stream that evolves dynamically at runtime based on observed coverage information, leveraging an ISS as a reference model in a tight co-simulation setting. Cross-Level Processor Verification via ...

[2] Coverage information is continuously updated based on the execution state of the ISS, and the novel concept of Coverage-guided Aging is employed to smooth out the coverage distribution of the randomized instruction stream over time, enabling a broad and deep coverage to find intricate corner-case bugs in the RTL core. Cross-Level Processor Verification via ...

[3] The verification framework comprises an Instruction-Injector, a Coverage-Observer, a Core-Adapter, the RTL-Core, the RTL-Memory, the ISS, and the ISS-Memory. Cross-Level Processor Verification via ...

[4] Experiments on the 32-bit pipelined RISC-V core of the MINRES The Good Core (TGC) series achieve a much more regular coverage distribution of the randomized instruction stream. Cross-Level Processor Verification via ...

[5] A prior ISS+RTL co-simulation approach supports arbitrary combinations of load/store and CSR instructions and infinite loops, but does not collect or employ runtime coverage information; it relies on a simple randomized test strategy, making it difficult to continuously achieve a broad and deep test coverage in endless instruction streams. Cross-Level Processor Verification via ...

[6] SymPrompt is a code-aware prompting strategy that deconstructs testsuite generation into a multi-stage sequence, each stage aligned with execution paths of the method under test, exposing relevant type and dependency focal context; it is implemented with TreeSitter and evaluated on a benchmark of challenging methods from open-source Python projects. Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM

[7] SymPrompt enhances correct test generations by a factor of 5, bolsters relative coverage by 26% for CodeGen2, and improves coverage by over 2x for GPT-4 compared to baseline prompting strategies. Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM

[8] For deep learning systems, a set of combinatorial-testing coverage criteria is proposed together with a CT coverage-guided test generation technique, addressing the large runtime state space of DL systems. Combinatorial Testing for Deep Learning Systems