EvoMaster
tcases
| EvoMaster | tcases | |
|---|---|---|
| 5 | 1 | |
| 428 | 201 | |
| 4.7% | 1.0% | |
| 9.8 | 7.3 | |
| 6 days ago | 25 days ago | |
| Kotlin | Java | |
| GNU Lesser General Public License v3.0 only | MIT License |
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EvoMaster
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EvoMaster 2.0: A Fuzzer for generating JUnit tests for REST, GraphQL and RPC APIs
Open-source on GitHub at: https://github.com/EMResearch/EvoMaster
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Anyone using AI for testing backend?
If you want to see an exampling of AI-based fuzzing of backend services, you can look at EvoMaster
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Experiences with automated unit testing generation tools?
if your Java project is a REST API, you can look at EvoMaster (https://github.com/EMResearch/EvoMaster) (DISCLAIMER: I am its author)
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What language should I choose for API automation?
If you want to generate the API tests automatically, you might want to have a look at EvoMaster. Disclaimer: I am its author.
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Does anybody know a simple algorithm for generating unit tests given a function's code?
Automated White-box test generation software: * https://github.com/EMResearch/EvoMaster -- for integration tests. * https://github.com/se2p/pynguin, https://pynguin.readthedocs.io/en/latest/user/quickstart.html -- unit test generation for python
tcases
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Does anybody know a simple algorithm for generating unit tests given a function's code?
Black-box test case generation software: * https://github.com/Cornutum/tcases
What are some alternatives?
evosuite - EvoSuite - automated generation of JUnit test suites for Java classes
openapi4j - OpenAPI 3 parser, JSON schema and request validator.
pynguin - The PYthoN General UnIt Test geNerator is a test-generation tool for Python
algebra-driven-design - Source material for Algebra-Driven Design
FsCheck - Random Testing for .NET
methods2test - methods2test is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories
PostmanTesting - This is my first repository created via POSTMAN with Github API.
HoloDB - HoloDB is an RDBMS seemingly filled with random data. This data does not actually take up any space in memory or on a volume (to use an analogy, it is as if the data set is projected as a hologram from a simple configuration). The base layer is an arbitrarily large, read-only data set that is readable and searchable, and yet fully consistent. Any pieces of data and index lookups are calculated on-the-fly. An optional second layer is built on top of this, allowing read-write access (stores differences while maintains consistency and searchability). You can start an arbitrarily large database in moments, with minimal effort; all you need is a YAML configuration file or some JPA entites.
austin-sbst - Automatically exported from code.google.com/p/austin-sbst