Automated Test Case Generation

Concept

Automated test case generation is the process of producing test cases for software systems without manual authoring, typically by combining symbolic analysis, constraint solving, model inference, or evolutionary search over a specification or model of the system under test.

First seen 7/7/2026

Last seen 7/7/2026

Evidence 1 chunks

Wiki v1

WIKI

Automated Test Case Generation

Automated test case generation refers to techniques and tools that derive executable test cases from a specification, model, or runtime behavior of a system, rather than having them written by hand. The evidence in the literature spans several paradigms: constraint-based generation from formal specifications, search-based generation guided by code coverage, and inference-based generation guided by learned models of system behavior.

Constraint-based generation from specifications

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Constraint Solver ← uses 97% 1e

A new constraint solver is presented for the automated generation of test cases from specifications.

specification language uses → 93% 1e

Test cases are automatically generated from specifications written in the specification language.

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CITATIONS

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[1] A constraint solver is used for the automated generation of test cases from specifications written in a language inspired by contract-oriented programming extended with finite state machines, enabling full test scenario generation via symbolic animation and a flexible CSP architecture with type builders. A Constraint Logic Programming Approach to Automated Testing

[2] For Industrial Internet of Things applications, an approach automates the testing process from threat modeling onward, closing the gap between threat modeling and automated test case generation to systematically produce security tests. Integrating Threat Modeling and Automated Test Case Generation into Industrialized Software Security Testing

[3] EvoMaster uses Evolutionary Algorithms to automatically generate test cases for microservices' REST APIs, and the MISH heuristic uses real-time automaton learning from microservice log events to guide test case generation, with empirical evaluation against MOSA on six real-world microservice benchmarks. Automated Test-Case Generation for REST APIs Using Model Inference Search Heuristic