methods2test VS tcases

Compare methods2test vs tcases and see what are their differences.

methods2test

methods2test is a supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java software repositories (by microsoft)
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methods2test tcases
1 1
119 201
3.4% 0.5%
0.0 7.1
5 months ago 28 days ago
Python Java
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

methods2test

Posts with mentions or reviews of methods2test. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-26.

tcases

Posts with mentions or reviews of tcases. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-26.

What are some alternatives?

When comparing methods2test and tcases you can also consider the following projects:

pynguin - The PYthoN General UnIt Test geNerator is a test-generation tool for Python

EvoMaster - The first open-source AI-driven tool for automatically generating system-level test cases (also known as fuzzing) for web/enterprise applications. Currently targeting whitebox and blackbox testing of Web APIs, like REST, GraphQL and RPC (e.g., gRPC and Thrift).

FsCheck - Random Testing for .NET

openapi4j - OpenAPI 3 parser, JSON schema and request validator.

algebra-driven-design - Source material for Algebra-Driven Design

austin-sbst - Automatically exported from code.google.com/p/austin-sbst

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.