I already published Fake-Bot on Medium, where I describe my program declaratively, and the bot figures out the implementation. This takes away the red (or green?) of the red-green-refactor cycle.
The article may suggest that this is thought on the unit-test-level - it isn't. It works on all the levels on the test pyramid.
How does the Fake-Bot know how to make tests green? Either by a table-based approach or - more interesting - by searching in a database. In the latter version, there is a database with functions, classes, modules, services. The entries are indexed by inputs and outputs of the entity. When I declare my function by inputs and outputs, Fake-Bot searches the database and interests the code for me.
Now comes the ultimate code reuse. Imagine I have an ecosystem like the npm-modules. But I don't have to be aware of which modules are available and which not. I neither have to integrate them manually. All this is done by a bot like it should be. I only state what I want by providing some examples.
What remains is the stuff I cannot automate. This is the place where I don't waste time anymore.
The article may suggest that this is thought on the unit-test-level - it isn't. It works on all the levels on the test pyramid.
How does the Fake-Bot know how to make tests green? Either by a table-based approach or - more interesting - by searching in a database. In the latter version, there is a database with functions, classes, modules, services. The entries are indexed by inputs and outputs of the entity. When I declare my function by inputs and outputs, Fake-Bot searches the database and interests the code for me.
Now comes the ultimate code reuse. Imagine I have an ecosystem like the npm-modules. But I don't have to be aware of which modules are available and which not. I neither have to integrate them manually. All this is done by a bot like it should be. I only state what I want by providing some examples.
What remains is the stuff I cannot automate. This is the place where I don't waste time anymore.
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