OSS (Open Source software) project developer prediction method based on Email networks

Abstract

An OSS project developer prediction method based on Email networks comprises the following steps that 1) different types of Email networks are constructed; 2) different network node ordering algorithms are used to calculate the different nodes and obtain corresponding characteristic values, and a network topology property is used to obtain the characteristic vector centrality and clustering coefficient of each node; 3) the characteristic values obtained via different algorithms and ranks of topology property parameters are normalized and serve as sample features; 4) part of the nodes serves as a sample and input to a machine learning classifier, and a Bayesian algorithm is used for learning; and 5) residual node samples are predicted. The method is aimed at the characteristic that an OSS project includes a lot of participants but a few core developers, developers in different OSS projects can be predicted effectively, and compared with a network node ordering algorithm, the accuracy is improved substantially.

Description

technical field [0001] The invention relates to the fields of network science and machine learning, in particular to a method for predicting developers of open source software projects based on Email networks. Background technique [0002] With the development of science and technology, many advantages of open source software (Open Source Software, OSS) projects are gradually recognized by people, reference 1 (Q.Xuan, M.Gharehyazie, P.T.Devanbu, and V.Filkov, "Measuring the Effect of Social Communication on Individual Working Rhythms: A Case Study of Open Source Software," in International Conference on Social Informatics, 2012, pp.78-85.1, that is, Q.Xuan, M.Gharehyazie, P.T.Devanbu, and V.Filkov, social situation on the working rhythm Impact Research: A Case Study of Open Source Software Projects, in International Conference on Social Informatics, 2012, pp.78-85.1). In order to effectively maintain the success of an OSS project, it is particularly important to use its pub...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the inability of existing machine learning methods to accurately predict whether a certain programmer will be promoted to the actual developer of the project, the purpose of the present invention is to predict the currently promoted developer of the project based on the Email communication data in the OSS project (from a mere discussant turned into an actual code contributor)

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