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:content {:text "over half (54%) of PRs are not linked to an issue when submitted, despite the fact that contribution guidelines of their repositories recommend linking."},
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:content {:text "To address the problem of lacking links between issues and PRs, several initial efforts based on open-source software have been dedicated to recommending links."},
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:content {:text " issues and PRs with similar content may not necessarily be related, while those with dissimilar content could indeed be linked."},
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:content {:text " solely relying on textual similarity to recommend issue-PR links may be insufficient and less effective."},
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:content {:text " it is necessary to explore how to organize such useful and complex knowledge for the task of predicting issue-PR links on GitHub"},
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:content {:text "given a pair of an issue and a PR, AIPL can suggest whether there can be a link"},
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:content {:text "corresponding connections will be established if a user submits an issue or a pull request to a repository, or if the user contributes to or forks the repository."},
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:content {:text "redicting the existence of a link between issue node vI n and pull request node vP R n in the test set (vI T , vP R T ), i.e, f (vI n, vP R n ) → y (1) where f is the function to learn and y ∈ {0, 1} is the label of the issue-PR link. y = 1 indicates the existence of a link between vI n and vP R n , and y = 0, otherwise"},
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:content {:text "Each element within this vector is populated with a randomly selected integer, drawn from a range between 1 and 10"},
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:content {:text "cosine similarity between node embeddings of VR learned by the method of Section 3.1.1."},
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:content {:text "For example, the embedding dimension of VU is 10, while the embedding dimensions of other node types are defined as 50. "},
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:content {:text "Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, pp. 4–24, 2019"},
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:content {:text "earn the comprehensive embeddings of issues and PRs"},
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:content {:text "o integrate the information under each metapath"},
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:content {:text "nalysis 12"},
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:content {:text "the set of nodes {repository concord-consortium /codap, user Maaartinus, repository folio-org/stripes-core} is the metapath-based intermediate nodes of node PR #103"},
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:content {:text "etapath P , this aggregation process encodes all metapath instances of P to aggregate the information of target nodes, metapath-based neighbors, and the context in between"},
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:content {:text "overall forward propagation process is shown in Algorithm 1."},
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:content {:text "sk-specified heterogeneous graph G, metapaths P and the number of attention heads K."},
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:content {:text "RQ1 Can our proposed AIPL effectively predict issue-PR links on GitHub?"},
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:content {:text "RQ2 Can AIPL be effective when specific portions of the heterogeneous graph or metapaths are removed?"},
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:content {:text "RQ3 How does the performance of AIPL vary when different thresholds or numbers of attention heads are applied?"},
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:content {:text "lthough some studies have released datasets for similar tasks, they have primarily focused on predicting links between issues and PRs within the same repositories. In contrast, our task aims to link issues and PRs across various repositories in GitHub."},
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:content {:text "multiple data sources, including information from issues, PRs, repositories, and users, which are absent in prior datasets. Therefore, we have chosen to construct new datasets,"},
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:content {:text "facebook/react and vuejs/vue"},
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:content {:text " 1) These two repositories host the most stars on GitHub, apart from some recommended lists, tutorials, and books"},
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:content {:text "2) they contain plenty of issues and PRs for our research (facebook/react host 27,229 issues and PRs and vuejs/vue host 13,064 by August 2023.)"},
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:content {:text "3) active communities are still dedicated to maintaining and developing these repositories and our work may help their contributors and subscribers."},
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:content {:text "randomly select 19 repositories from the above candidates"},
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:content {:text "we filter out non-English issues and PRs"},
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:content {:text "we remove the issues and PRs that are submitted by bots, since these issues and PRs might lack essential content and primarily serve as automated notifications"},
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:content {:text "e delete duplicate issues and PRs to ensure the uniqueness of the items in this dataset"},
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:content {:text "proceed "},
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:content {:text "we randomly match an issue from the aforementioned positive samples with a PR"},
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:content {:text "we introduce manual inspection to ensure the quality of our dataset"},
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:content {:text "Cohen’s kappa agreement is calculated to be 0.7332, indicating an acceptable level of consistency in the results. "},
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:content {:text ". Finally, all the authors double-check the annotation results to ensure a 100% agreement rate"},
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:content {:text "# of {I-PR} 8,457 3,655"},
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:content {:text "The remaining data and linkage labels are used to train our model"},
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:content {:text "90/10 chronological split on the remaining dataset for the training set and validation set"},
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:content {:text "hen, given a target node such as an issue, the algorithm would explore the graph by performing random walks to generate a sequence of PR nodes."},
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:content {:text "embeddings"},
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:content {:text " In cases where the content of certain repositories, issues, or PRs is empty, we initialize their 50-dimensional vectors with random values [30], [38], [39]. "},
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:content {:text "we plan to take our approach one step further by employing a larger-scale dataset to predict links from small and new repositorie"},
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:extra {:page 7}}
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