In machine learning it has a method that automatically performs many of the frustrating and repetitive tasks involved in model development. Automated Machine learning was developed to rise the productivity of data scientists, analysts, and developers and to make machine learning more reachable to those with less data expertise.
Machine learning automation is dominant because it authorize organizations to appreciably turn down the knowledge build resources need to instruct and implement machine learning models. This is used by productively by institution with less domain knowledge, hardly any computer science skills, and less mathematical proficiency. By this it can reduces the pressure on discrete data scientists in addition on organizations to find and hold on to those scientists. Machine learning automation let down the must have for entry to model development, authorize industries that were formerly incompetent to leverage machine learning to do so. It can creates chances for alteration and nourish the competitiveness of markets, driving advancement.
Tool of Automatic Code Generation:
The automatic code generation tool provide many functions like generation and completion of multiple programming languages for all programmers.
Presently, there are two different types of open source automated generation tools available that include template based tools and machine learning tools.
- Template based tools
In this tool some syntax information of the code is predefined by entity class required that given by development framework. Then this undefined source code is generated automatically. By this we can say that, the template of the code framework is established to generate the desired structure of code.
Java code tool is also used for automatically generate source code for Java language. This is also used to solve many data management work in regular projects.
- Machine learning based tools:
Automatic code generation tool based on machine learning. With deep neural network model which is trained by enormous domain source code with strict filtering aiXcoder that can be used to encoding patterns and rules of information for code generation. aiXcoder used for understanding of developer’s programming habits and record the common program patterns, API call sequence and so on.
The main metric model of automatic code generation that based on the standard and productivity is suggested by this. Three source code automatic generation tools that have to do test some source code particles in a Java source code files. Although, the maximum length of common string. Java language was tested by this process of measurement research. The analysis measurement results can show that the metric model of automatic code generation is reasonable, which is very helpful to measure the automatic code generation.