Analysis and Prediction of Programming Language Occupancy Based on AdaBoost

With the advent of computers, people need computers to help people solve some problems. In the process of solving the problem, you will encounter the problem of programming. In order to help people use appropriate programming languages to complete tasks, this paper proposes a method to analyze and p...

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Bibliographic Details
Published in2022 2nd International Conference on Networking Systems of AI (INSAI) pp. 57 - 65
Main Authors Gong, Yang, Zhang, Pan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2022
Subjects
AdaBoost
Computer languages
Computers
Data models
forecast
Integrated learning
Prediction algorithms
Predictive models
programing language
Programming
Tiobe
Training
Online AccessGet full text
DOI10.1109/INSAI56792.2022.00021

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Summary:With the advent of computers, people need computers to help people solve some problems. In the process of solving the problem, you will encounter the problem of programming. In order to help people use appropriate programming languages to complete tasks, this paper proposes a method to analyze and predict the share of programming languages based on AdaBoost. First, collect the historical data of the world authoritative Tiobe programming language ranking list; Then the data table is visualized; Then, AdaBoost algorithm is used to model and train the share of the top five programming languages (Python, C, Java, C++, C #); Finally, enter the data to be predicted and return the predicted value of the model. After many times of training and testing, the method has a good prediction result, which can be used to analyze and predict the share of programming languages.
DOI:10.1109/INSAI56792.2022.00021