An Approach Of Automatic Fork Summary Generation In Open Source Community Based On Feature Extraction

At present,distributed collaborative development based on P/R has become an important software development method in open source community.Because of the openness,transparency and parallelism of the software development in P/R model,it is difficult for developers to obtain the complete Fork profile of the whole project,and therefore to know whether other developers have accomplished the same or similar development tasks,which are prone to duplicate contributions and redundant development.Therefore,this requires developers to be able to obtain Fork information intuitively,comprehensively and efficiently,but the existing Fork tools cannot meet these requirements.To solve this problem,this thesis proposes an automatic generation method of Fork summary to help project managers strengthen project management,avoid redundant contributions,and enhance cooperation and communication among developers.Our method firstly crawls Issue data with feature and Bug label information in open source community,and trains a classifier model with random forest method to classify Fork features.Then we collect the data of Fork branch's software development activities and use Text Rank algorithm to generate detailed Fork information to explain the main purpose of Fork activity.Finally,a set of combination rules and corresponding algorithm are designed to integrate Fork's categories,features and other information together to form a complete Fork summary.In order to validate the effectiveness of our method,30 groups of manual tests and60 groups of actual live study were conducted on Github.The results show that the accuracy of Fork summary generated by this method is 67.2%.76% of project managers believe that Fork summary can help better manage projects,strengthen communication and cooperation in the experiment.