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- AI-based Clustering of Similar Issues in GitHub’s 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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- I-based Clustering of Similar Issues in GitHub’s Repositories
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- The attractive repositories on Github receive a large number of issues daily.
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- Assigning similar issues individually to different developers for validating and fixing introduces inconsistencies when asynchronously independent developers fix them, in addition to slowing the fixing process.
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- 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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- machine learning algorithm-based approach has been proposed to support issue management on GitHub by grouping similar issues together
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- adding new features, reporting bugs to be fixed, or asking a question about the capability of the software.
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- These issues differ in their nature and quality (asking for support, for improvement of a functionality, bug reporting). Therefore, the issues management in such cases will be harder
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- (i) to speed up the process of fixing issues and reduce the turnaround time for fixing,
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- 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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- M. Borg, L. Jonsson, E. Engstrom, B. Bartalos, A. Szab’o, Adopting automated bug assignment in practice: A longitudinal case study at ericsson, ArXiv abs/2209.08955.
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- Hamzeh Eyal Salman
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- o consolidate similar issues into a single, well-defined issue that covers all the variations and aspects of the problem
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- 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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- . Liao et al. [7] studied the impact of labeled or tagged issues on their management using six popular repositories
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- GitGub
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- ull-request (PR) 
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- ssue reporting
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- Figure 1:
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- he 
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- ssues1,2,3, 4 
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- Figure 2:
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- cookie handling
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- there is no research work in the literature addressing the problem of clustering similar issues into groups in GitHub. 
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- natural language processing (NLP
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- information retrieval (IR)
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- machine learning (ML)
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- 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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- Jacard, cosine, and dice measur
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- ug reports i
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- This ranked list allows triggers to compare the incoming bug report with this list to discover the duplication
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- uplicate bug report detection with a combination of information retrieval and topic modeling, i
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- 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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- Then,175 T
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- As mentioned above, these important features are the title and body of each issue.
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- ground-truth dataset.
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- refuted
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- owever, such a dataset does not exist yet.
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- duplicate clusters of issues, as 100% duplicate issues are similar issues, too
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- ese clusters are diverse to ensure free-bias evaluation
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- RQ1: To what extent are the identified issue clusters correct?
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- to what extent the member issues of a cluster are related to each other (similar). 
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- RQ2: To what extent are the identified issue clusters complete?
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- AgglomerativeClustering
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- 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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- to return a number of relevant issue clusters equal to the number of ground-truth clusters
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- credability
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- Internal threat
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- External threat
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- 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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- clustering process in this research work is guided by two important features extracted from the issue’s report. These features are the title and body of each issue
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- Figure 4: An example of dendrogram tree with cutting line.9
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- without a prior assumption about the number of clusters
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- In this research work, this cutting point is set after many tries to find255 reasonable similar issue clusters based on title and description matching
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- A cluster from the dataset that shares a maximum number of issues with the extracted cluster is the target and is called a ground-truth cluster
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- The higher Recall value you have, the higher completeness cluster is extracted, and vice-versa. 
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- For each extracted cluster (EXC),
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