Research Of Issue Units Network

Issue reports and Pull Requests(PRs)are two important kinds of artifacts of software projects in GitHub(we refer to issue reports or PRs as issue units).Existing studies have demonstrated the value of such links in identifying complex bugs and duplicate issue reports.However,there are no broad examinations of why developers leave links within issues/PRs and the potential impact of such links on software development.To fill this gap,we conducted the first empirical study to explore the characteristics of a large number of links within 642,281 issues/PRs of 16,584 popular(>50 stars)Python projects in GitHub.Specifically,we first constructed an Issue Unit Network(IUN)by making use of the links between issue units.Then,we manually checked a sample of 1,384 links in the IUN and concluded six major kinds of linking relationships between issue units.For each kind of linking relationships,we presented some common patterns that developers usually adopted while linking issue units.By further analyzing as many as 423,503 links that match these common patterns,we found several interesting findings which indicate potential research directions in the future.Finally,this paper explores the detection of repeated pull requests.This paper proposes the detection of repeated pull requests based on BM25 method.Considering that BM25 only uses the characteristics of text,we also proposes that BM25 LDA comprehensively use the text characteristics and potential topic features of text.Interesting findings are proposed in IUN,which include detecting cross-project duplicate issue reports,using IUN to help better identify influential projects and core issue reports.In the experiment of detection of repeated pull requests,the BM25 LDA is up 3.4% over the existing method at recall-rate@20.