Project popularity analysis method based on mixed effect linear regression model

Abstract

Aiming at the problem that the evaluation of the project popularity has one-sidedness since the defect report and the feature report are separately explored for the existing research, the invention provides a project popularity analysis method based on a mixed effect linear regression model, comprising: by collecting project data from GitHub and then using statistical analysis and regression modeling, giving the influence relation on the project popularity by the number of defect reports and the number of characteristic reports in the project, analyzing the relation between the improvement ofthe project popularity and the defect reports and feature reports through the difference of the influence factors of the defect reports and feature reports in the project to the project popularity; and progressively performing four dimensions of analysis on the description diversity of the defect reports and feature reports to find out the difference between the defect reports and the feature reports in describing the diversity. The invention comprehensively studies the project popularity by analyzing the difference between the number of defect reports and the number of feature reports in theproject, and may comprehensively evaluate the popularity of the project.

Description

technical field [0001] The invention belongs to the field of computer open source software analysis, in particular to a method for analyzing the impact of bug issues and feature issues on item popularity in the project development process. Background technique [0002] Software development is a complex process with many steps and involved developers. Code defects (bugs) or new features (features) usually appear during the software development process. Therefore, bug reports (bug issue) and feature reports (feature issues) are two very important factors in the software project development process. [0003] The number of bug issues and feature issues in projects with different goals and requirements will be different, and the difference in the number of bug issues and feature issues will have a certain impact on project development, such as the popularity of the project. Existing research mainly explores bug issues and feature issues separately, and the judgment of project po...

AI Technical Summary

Problems solved by technology

[0010] Most of the existing researches in the present invention are to explore bug issue and feature issue separately, and rarely compare the difference between the number of bug issues and feature issues in the project to comprehensively study the influence on the popularity of the project, so the popularity of the project cannot be fully evaluated. The present invention provides a project popularity analysis method based on a mixed effect linear regression model, by collecting project data from GitHub, using the label of the GitHub project issue to judge whether the issue is a bug issue or a feature issue, and then using statistical analysis and regression modeling , the influence relationship between the number of bug issues and the number of feature issues in the project on the popularity of the project is given, and the influence of the number of bug issues and feature issues in the project on the popularity of the project is different, and the relationship between the increase in project popularity and the bug issue and feature issue is analyzed relationship to comprehensively assess item popularity

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