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:content {:text "AI-based Clustering of Similar Issues in GitHubs Repositories Article Type: Full Length Article Keywords: Similar Issues; GitHub; Machine Learning; Maintenance; Clustering Corresponding Author: Hamzeh Eyal Salman Mutah University Karak, JORDAN Corresponding Author Secondary Information: Corresponding Author's Institution: Mutah University"},
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:content {:text "grouping similar issues into clusters and assigning each cluster to the same and appropriate developer/team speeds up the fixing process."},
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:content {:text "ii) when related issues are grouped together, this speeds up the assignment process by finding the appropriate reviewer(s) for a cluster instead of25 individual issues, especially, if the assignment process is done manuall"},
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:content {:text "M. Borg, L. Jonsson, E. Engstrom, B. Bartalos, A. Szabo, Adopting automated bug assignment in practice: A longitudinal case study at ericsson, ArXiv abs/2209.08955."},
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:content {:text "o consolidate similar issues into a single, well-defined issue that covers all the variations and aspects of the problem"},
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:content {:text "The difference between similar and duplicate issues is that the former refers to a set of issues with some common characteristics or are closely related, but they are not30 necessarily exact duplicates."},
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:content {:text ". Liao et al. [7] studied the impact of labeled or tagged issues on their management using six popular repositories"},
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:content {:text "there is no research work in the literature addressing the problem of clustering similar issues into groups in GitHub. "},
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:content {:text "The idea behind using IR and NLP is to find textual overlapping between bug report queries and already existing report queries in the repository"},
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:content {:text "This ranked list allows triggers to compare the incoming bug report with this list to discover the duplication"},
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:content {:text "uplicate bug report detection with a combination of information retrieval and topic modeling, i"},
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:content {:text " In this study, title and body features are only considered during the clustering process while Label is ignored as it is repetitive and missed in many issues."},
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:content {:text "As mentioned above, these important features are the title and body of each issue."},
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:content {:text "ground-truth dataset."},
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:content {:text "owever, such a dataset does not exist yet."},
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:content {:text "duplicate clusters of issues, as 100% duplicate issues are similar issues, too"},
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:content {:text "ese clusters are diverse to ensure free-bias evaluation"},
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:content {:text "RQ1: To what extent are the identified issue clusters correct?"},
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:content {:text "to what extent the member issues of a cluster are related to each other (similar). "},
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:content {:text "RQ2: To what extent are the identified issue clusters complete?"},
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:content {:text "AgglomerativeClustering"},
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:content {:text "As a summary, the AHC algorithm can extract relevant similar issue clusters from GitHub repositories depending on only two issue features: title and description."},
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:content {:text "n this paper, a machine learning-based approach is proposed to support the issue tracking system on GitHub via grouping similar submitted issues together"},
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:content {:text "clustering process in this research work is guided by two important features extracted from the issues report. These features are the title and body of each issue"},
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:content {:text "Figure 4: An example of dendrogram tree with cutting line.9"},
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