To start with, the idea is to generate code in PHP, Ruby on Rails, Django, Java, Python-based AI Coder can generate code in python and Django or any other frameworks. Typically we provide details regarding the software and how it works. This takes effort which is equal to writing the code. AI Coder needs basic info about the entities involved in the code. It can train itself by reading zillions of human-written code. It can read from a repository and generate code based on the components. It is useful for creating samples of code and unit tests for software frameworks. Data generation for testing is another feature of AI Coder. AI Coder can take input as rules, tools, definitions, protocols, database support and or other software support. It can be enhanced to support developed software API for code generation. AI Coder is based on neural sketch learning. Neural sketch learning is related to an ANN trained to identify the code level patterns from different software.
Application configuration will provide the ability to customize certain aspects of the platform like screens, data fields, workflows, etc. The application will have development related configuration related to managing metadata and programmatic updates.
The sample configuration is provided which has the form, form fields, views, entities used in the system.
<?xml version=”1.0" encoding=”UTF-8"?>
User
1
UserId
UserName
Password
The application will have the user view. User view will have the view fields which are UserId, UserName and Password.
User
1
UserName
Password
The form configuration for User view will have form fields UserName and Password.
User
1
UserId
UserName
UserName
Password
The update view will have formno, formIdField, searchFormField and the form fields UserName and Password.
User
1
UserId
UserName
UserName
Password
The delete view will have formno, formIdField, searchFormField and the form fields UserName and Password.
The configuration below shows the web service request and response schemas. User service will have getAll, getUser, InsertUser, UpdateUser and DeleteUser methods.
<?xml version=”1.0" encoding=“UTF-8”?>
wsdl:types
Get All method request and response definition is shown above.
Get User method request and response definition is shown above.
updateUser method request and response definition is shown above.
insertUser method request and response definition is shown above.
deleteUser method request and response definition is shown above.
The schema definition for the User complex type is shown below.
/wsdl:types
User complex Type elements are shown above.
/wsdl:part
/wsdl:message
/wsdl:part
/wsdl:message
deleteUser response elements are shown above.
/wsdl:part
/wsdl:message
/wsdl:part
/wsdl:message
updateUser response elements are shown above.
/wsdl:part
/wsdl:message
InsertUser request elements are shown above.
/wsdl:part
/wsdl:message
deleteUser request elements are shown above.
/wsdl:part
/wsdl:message
InsertUser response elements are shown above.
/wsdl:part
/wsdl:message
getUser request elements are shown above.
/wsdl:part
/wsdl:message
getAll response elements are shown above.
/wsdl:part
/wsdl:message
getUserById response elements are shown above.
/wsdl:part
/wsdl:message
getUser response elements are shown above.
/wsdl:input
/wsdl:output
/wsdl:operation
User service elements are shown above.
/wsdl:input
/wsdl:output
/wsdl:operation
getUser operation input and message elements are shown above.
/wsdl:input
/wsdl:output
/wsdl:operation
updateUser operation input and message elements are shown above.
/wsdl:output
/wsdl:operation
getUserById operation input and message elements are shown above.
/wsdl:input
/wsdl:output
/wsdl:operation
insertUser operation input and message elements are shown above.
/wsdl:input
/wsdl:output
/wsdl:operation
/wsdl:portType
deleteUser operation input and message elements are shown above.
The application configuration helps the framework to learn the semantics of the application. The application has user interfaces, business logic, services, classes, data types, methods, control flow, exceptions and type based entities.
The patterns are identified by the neural sketch-based framework of the AI coder bot which helps in creating, rendering and displaying the applications. The application needs to satisfy the constraints specified in the configuration file. The neural sketch framework is based on the combination of neural learning and combinatorial search. The source code in the framework has the capability to create tree-based syntactic models and sketches.
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