Randomized Testing

Technique

Randomized Testing (RT) is a software and hardware verification technique in which a system under test (SUT) is exercised with test cases drawn from random distributions over the input domain rather than from manually written or model-derived test suites. The technique is well established, has been applied across embedded systems, databases, mobile applications, cloud APIs, and processor designs, and has been refined by variants such as Adaptive Random Testing (ART).

First seen 6/9/2026

Last seen 6/9/2026

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WIKI

Randomized Testing

Randomized Testing (RT) is a testing method in which a system under test is driven by test cases sampled from random distributions over its input domain, instead of by hand-written or specification-derived tests. Because generation is decoupled from any specific failure hypothesis, randomized testing can explore very large input spaces cheaply and is frequently used as a complement to directed verification.

Core characteristics

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NEIGHBORHOOD

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graph · Randomized Testing · depth=1

RELATIONSHIPS

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Randomized Testing of RISC-V CPUs using Direct Instruction Injection ← uses 100% 1e

The paper employs Randomized Testing as a core technique.

LINKED ENTITIES

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Randomized Testing of RISC-V CPUs using Direct Instruction Injection USES Extracted graph relationship

CITATIONS

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[1] Random testing (RT) is a well-studied testing method. A Survey on Adaptive Random Testing

[2] Randomized testing has been widely applied to embedded software systems, SQL database systems, and Android applications. A Survey on Adaptive Random Testing

[3] Adaptive Random Testing (ART) was introduced in 2001 and aims to enhance RT's failure-detection ability by more evenly spreading test cases over the input domain. A Survey on Adaptive Random Testing

[4] The ART literature includes various approaches, implementations, assessment and evaluation methods, and applications, and has been comprehensively surveyed. A Survey on Adaptive Random Testing

[5] Autotest is a random test generator for cloud APIs that reads the API specification and deduces a model used in test generation. Random Test Generation of Application Programming Interfaces

[6] A best practice when Autotest reveals an API specification problem is to add an appropriate test to the regression once the problem is revealed and solved. Random Test Generation of Application Programming Interfaces

[7] The paper 'Randomized Testing of RISC-V CPUs Using Direct Instruction Injection' applies randomized testing to RISC-V CPU verification using direct instruction injection. Randomized Testing of RISC-V CPUs Using Direct Instruction Injection (Joannou et al., IEEE Design & Test, 41(1):40-49, Feb 2024)