488 lines
19 KiB
Markdown
488 lines
19 KiB
Markdown
file:: [icse2023-paper223_1667256095764_0.pdf](../assets/icse2023-paper223_1667256095764_0.pdf)
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file-path:: ../assets/icse2023-paper223_1667256095764_0.pdf
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- technical debt prediction, software remodularization.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 6360511f-7cce-4dbc-879b-097f16c65cf8
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- engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clusterin
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605139-2500-4cbc-af84-cbd2cbd915b7
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- We revealed seven clusters of software repositories and relate them to developers’ activity.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 6360515e-cd88-43f3-b719-5a0679a60096
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- facilitating the decision making process
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605386-b60d-4007-b1ac-3523bfb830e2
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- retrospective analysis can affect the decision-making process in a project, and improve the quality of the software system being developed.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605399-e62c-4875-b7ca-d464ad153b98
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- classify software repositories as “engineered” or “not engineered”
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 636053a2-f8ef-4fc7-b214-e4ff43c2c630
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- For this reason, many studies use unsupervised learning techniques such as clustering to determine the similarity between software repositories and to provide ground for repository analysis.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 636053c0-72e4-4d91-bc23-ed946f6c8dde
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- growth patterns of GitHub repositories’ number of stars
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 636053e0-4277-46f7-a522-8b875b33f798
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- how people star the repositories and what this starring is attributed to.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 636053e6-afd3-4606-8588-6de64909e7cb
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- a suite of different clustering methods providing different partitions of data and aggregate their results into a single consolidated clustering
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 63605402-2ccb-4547-9c0b-2866584fe167
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- there is no prior study applying the consensus clustering approach to analyze software repositories.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 6360541a-4261-4830-98b0-cbfcf6990c1b
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- consensus clustering
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605423-2ba3-4cde-bc94-ec725d176c43
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- consensus result
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605438-b250-48d4-bbe9-d3726e9cf59d
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- consensus clustering
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605441-64bb-48ea-ac04-88566f521aeb
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- it is possible to qualify a software repository by using its quantitative metrics.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 63605450-8e08-400c-80e4-03dda2e2e510
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- studying the software ecosystems and identifying important indicators about development activity in software projects.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 636058b1-25bd-476a-9d34-212e3821013c
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- epositories are always attributed to one of the developers’ activity clusters;
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 636058c9-6d94-4f29-90e8-2ad90f1621b0
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- proposed methodology to cluster software repositories
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 63605912-ee07-4478-b34d-ec98886f3d1e
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- analyze the results and then details significant parts of the methodolog
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 63605919-b2b4-459d-9b21-1d354cbcc75b
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- clustering used in related works corresponds to the proposed manual classifications and whether it addresses the shortcomings of manual approache
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 6360594c-3a9b-4af4-b3bb-dd328e7d7d6b
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- organization
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 63605978-fc33-4b2f-acd2-0ecdd4e76f0b
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- utility
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 6360597a-6eee-469b-8c29-b77771f8af98
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- ound software engineering practice
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ls-type:: annotation
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hl-page:: 2
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hl-color:: yellow
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id:: 63605987-114a-4e0a-aeb8-9451aceb70d2
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- the limitation of the domain-based classification is that several domains can be grouped together into one or divided into a number of smaller domains,
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 636059d7-5335-4b66-80a6-20d2aa78cbce
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- such grouping is subjective and it is hard to precisely determine the number of separate domains in advance
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63605a02-dbe9-495e-823c-01346f7410b8
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- Commit-related event
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63605a3c-96e2-402c-bd7c-f0a80cfd81d5
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- Issue-related event
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63605a3f-9b35-4c02-8592-1bfd08cbbf84
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- Pull request-related even
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63605a47-46c5-41c7-8e8f-2e8ccdba8bc5
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- days between open and close
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ls-type:: annotation
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hl-page:: 2
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hl-color:: yellow
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id:: 63605a6c-25f4-44f0-968f-cbe888236d04
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- help prevent potential problems early on, encourage discussion where it is needed, and give important pointers to events in a project’s history to be reviewe
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 63605aa1-3201-480d-b1c9-b4242945a74b
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- commit or source code activity
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 63605abd-6a04-4a3d-9345-fac50d4947d9
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- ,
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ls-type:: annotation
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hl-page:: 3
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hl-color:: red
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id:: 63605ae8-b38c-4c80-8920-475d6fb44415
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- developers “value simple and easily understandable metrics over complex one
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 63605b69-bb28-4f08-981a-f6d37054bee0
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- conduct
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 63605bd3-c911-45f7-87f5-6faaa38c3a00
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- identify useful metrics that indicate something unusual in the resulting clusters of repositories.
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 63605be3-ac81-445d-b044-e22f12fdbad6
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- e did not identify a prior study that would analyse opensource software repositories from the perspective of developers’ community interest
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ls-type:: annotation
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hl-page:: 4
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hl-color:: green
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id:: 63605c36-d037-4891-b730-770189b2b883
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- with the intention of identifying high-quality GitHub repositories and removing noise
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 636131f4-57d8-456b-bf5d-2cb9b8276579
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- bias towards industry giants and their repositories under the assumption that they use the “sound software engineering practices”.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63613217-bc91-47ac-be4c-0d490b54e018
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- web libraries and framework
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63613242-dafe-43b3-8b29-dca42f6ed3dd
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- non-web libraries and frameworks
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 63613248-6b6b-4376-b792-fc0d8d0d3ac9
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- software tools
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 6361324c-5f99-4bcb-a060-a5eed04473f8
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- application domain is useful for understanding the clusters of GitHub repositories based on number of stars history over time
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ls-type:: annotation
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hl-page:: 2
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hl-color:: yellow
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id:: 636132da-13ae-47ef-808a-0f664db660c8
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- using number of stars evolution, these domains can be grouped into one, resulting into three-domain repository classification
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 636132fa-1c7d-42a5-81f4-41e0e62e4ecd
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- alone are not sufficient to communicate all the information developers care about in a project, which means that additional data related to issues and pull-requests have to be collected and analyzed.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 636133cf-b926-4815-8783-01026d0eda22
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- e try to identify the metrics that would characterize software repositories qualitatively and signal about potential anomalie
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 636133df-d256-405b-8700-fd45a3991aff
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- though simple and easily understandable metrics might not be enough to uncover the underlying data patterns
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 636138c2-e2b0-457e-9c23-c2cac8147967
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- To address this, we conduct an automatic clustering of the repositories based on a set of GitHub metrics instead of relying on the simple “extreme outlier” definition of anomaly, and then try to identify useful metrics that indicate something unusual in the resulting clusters of repositories.
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ls-type:: annotation
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hl-page:: 3
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hl-color:: yellow
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id:: 636138f9-0dfa-417c-be6c-2431211ee381
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- classifications
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ls-type:: annotation
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hl-page:: 3
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hl-color:: yellow
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id:: 63613b31-2d97-45ba-83d6-1832eb468f6e
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- identifying similar projects to facilitate reuse and sharing knowledge among software projects
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ls-type:: annotation
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hl-page:: 3
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hl-color:: purple
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id:: 63613b44-f788-4319-9ac9-fa5dbf8ba968
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- groups of software projects with similar characteristic from the defect prediction point of view
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 63617dda-1e6f-4b64-95ab-def488e0214d
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- to group the libraries that are most frequently co-used together by clients in order to relieve developers from manual analysi
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 6361a152-a6fa-4925-aa5c-5aa8ff9c6d97
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- This clustering has limited internal validity as authors used only one cluster validity index
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ls-type:: annotation
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hl-page:: 3
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hl-color:: green
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id:: 6361a200-ed7d-4159-9c04-a95cd844e029
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- however we believe that this is an important aspect since the knowledge about this interest would help to better understand not only the state of the repository itself but also of the correspond software development project
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a236-c101-4aca-ab16-dd6c32dedd30
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- In addition, the proposed studies have limitations in terms of validity of the clustering results since most of them use a single clustering technique.
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ls-type:: annotation
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hl-page:: 4
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hl-color:: yellow
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id:: 6361a23a-7928-4740-9097-e9d888c6a30a
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- multiple clustering techniques
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a25d-7733-463d-bae1-0a4469d51fba
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- cluster validity indices
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a261-c7bb-470f-abef-17cfb4dbff06
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- consensus clusterin
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a264-afd2-4600-beb1-e1c398aaf9f6
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- methodology
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a310-b78a-4e31-bba3-bc85211b9ee9
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- three step
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a312-64bd-4d06-a063-4ee9979d725f
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- data collection and preparation
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a319-e9cc-40f0-83c3-67ab99ed6ff5
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- n metrics from them and apply the following algorith
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a349-186b-4b0e-89bb-75d97f364683
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- We normalize each metric to the [0,1] range
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a34d-d5bd-4244-b0fe-48738050a809
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- We group repositories into c clusters based on the n metrics using m clustering algorithms with different values of parameters that we tune.
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a358-8e72-4021-a719-98bc277002f1
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- For each grouping resulting from the previous step, we calculate three cluster validity indices and obtain at most three best groupings (in the sense that each groping optimizes a certain validity index) for each algorithm
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a368-ef98-4e7a-941b-ccab051a6921
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- We match the calculated cluster prototypes to compare them and the clusters they represent across P sets of clustered repositorie
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ls-type:: annotation
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hl-page:: 4
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hl-color:: green
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id:: 6361a53b-6119-4fd4-8afc-6cca08ad3fa6
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- three cluster validity indices on the clustering results produced by m clustering algorithms run on the metrics data.
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ls-type:: annotation
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hl-page:: 4
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hl-color:: purple
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id:: 6361a575-1ce1-4f95-8661-356e7e27ad70
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- if all three showed the same solution or different solutions, we would consider only one or three unique solutions correspondingl
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6361a8b6-7e82-4065-84fe-22f6e31336c8
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- Silhouette
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6361a8c1-1416-41f6-ae53-6d1498422c58
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- silhouette coefficient
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6361a8da-45b4-450b-b2aa-f326c3bf613d
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- Consensus clustering (also called cluster ensembles) provides improved quality of solution and robust clustering as compared to using a single clustering metho
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6361a901-c28a-4c07-b37d-348b6822fb3c
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- graph and hypergraph based as well as non-negative matrix factorization based consensus clustering methods as they are the most popular among the studies related to consensus clustering and are easy to understand and implement
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ls-type:: annotation
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||
hl-page:: 5
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hl-color:: purple
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||
id:: 6361a919-f0bf-4d1a-9be4-8ad5c4226b4c
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- the fraction of clusterings in which two software repository metrics vectors xi, xj are in the same cluster
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6361a95e-be8a-4797-8b4f-91c1341788a2
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- number of stars being in range (100, 200), number of forks – in range (50, 150), size being less than 2100 kilobytes.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 6361a9a1-1c04-4b50-ae8c-8c77bbe9f0e8
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- P = 7
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ls-type:: annotation
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||
hl-page:: 7
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hl-color:: purple
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id:: 6361a9ad-2bfa-4cf6-a57a-3a32907b18ee
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- N = 1659
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 6361a9d4-5737-4f4a-82b9-b41410a91323
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- n = 28 metrics
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 6361a9d8-7af1-4c70-8578-6a9baf40a4aa
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- n each set of repositories we have identified optimal groupings using the three clustering algorithms and the three validity indices
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 6361aa12-de47-4d1e-a25a-c352fb234949
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- The algorithms that correspond to the underlined values were used to get a single consensus clustering in each corresponding set of repositories.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: purple
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id:: 6361aa4c-d051-40ce-8705-63f11039baea
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- After calculating a single clustering for each of the repository sets, we calculated the prototypes according to our methodology.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 6361aa77-e6d4-4f95-a023-321ae1710740
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- mixed integer programming solver“Coin-or branch and cut
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 6361aa89-48d1-4d88-ba91-e5fea64f0fe0
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- this indicates the presence of more than one subcluster inside the cluster c2, and for this reason we repeat the clustering, however, this time without the division to P subsets as in c2 we have only 133 repositories.
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ls-type:: annotation
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hl-page:: 7
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hl-color:: green
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id:: 6361ab67-813f-4c37-96d7-e700bb94cfbb
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- Division of the second cluster to subclusters
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ls-type:: annotation
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hl-page:: 7
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hl-color:: red
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id:: 6361ab98-731d-40f3-8e9e-14e1299dbaaa
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- Processing of the Repository Sets
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ls-type:: annotation
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hl-page:: 7
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hl-color:: red
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id:: 6361ab9f-4795-4f0f-909f-9a607f164550
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- Cluster c21 prototype represents a repository with low values of total created and total (aggregated across the whole history in the dataset) closed issues
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ls-type:: annotation
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hl-page:: 7
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hl-color:: yellow
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id:: 6361abe9-fa05-446b-8071-82d965ece802
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- c21 represents repositories with relatively small activity, while other clusters represent different types of activity in repositories, such as sudden peak of developing activity lately (cluster c22, where the metrics for the past two weeks have noticeably larger values than for the past month);
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ls-type:: annotation
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hl-page:: 8
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hl-color:: purple
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id:: 6361ace2-d55d-41a1-af7e-8be05673300f
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- initial steady development, where the repository has been launched only recently and there is an intense development activity going on;
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ls-type:: annotation
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hl-page:: 9
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||
hl-color:: green
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id:: 6361ad6b-73b7-4eab-8d90-9acf06226765
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- active developmen
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ls-type:: annotation
|
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hl-page:: 9
|
||
hl-color:: purple
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id:: 6361ad71-ec4f-46ff-9351-d5099f7d6635
|
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- However, the Gartner use their hype cycle to describe the development of emerging technologie
|
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ls-type:: annotation
|
||
hl-page:: 9
|
||
hl-color:: blue
|
||
id:: 6361adc5-eb08-40b7-b5e2-0e6a1ac34078
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- if the software repositories can be grouped into clusters based on their metrics to gain dedicated practical insight
|
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ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: green
|
||
id:: 6361adeb-0f42-479c-836d-c5d1381e181e
|
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- each subset into two clusters
|
||
ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: green
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id:: 6361ae1a-cf41-4be6-8311-414ba70c8192
|
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- then measured the discrepancy value between the resulting clusters
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ls-type:: annotation
|
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hl-page:: 10
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||
hl-color:: green
|
||
id:: 6361ae20-67f9-461b-aed0-53b3e386217f
|
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- identify that there are more than two clusters inside our repository metrics dataset
|
||
ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: green
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||
id:: 6361ae2a-53a2-499c-873a-6b255483c6d3
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- We mapped the results of the data analysis to the Gartner hype cycle to facilitate the link between the analysis and business perspective part.
|
||
ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: purple
|
||
id:: 6361ae3c-64ab-4f6f-b979-30b5ca6db9bc
|
||
- seven clusters of different types of repositories in terms of developers’ activity, of which the majority turned out to be either abandoned or having relatively low activity, while the rest corresponded to different types of developers activity
|
||
ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: purple
|
||
id:: 6361ae46-1465-47ea-9158-9fb49dc01089
|
||
- expanding our set of metrics to include metrics related to pull requests, comments, releases, and workflows to see if our clustering based on both code and process metrics will be able to predict whether
|
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ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: purple
|
||
id:: 6361ae5d-3548-4fda-89f3-8ebaf512de2a
|
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- In addition, we also plan to study the clustered repositories in detail by inspecting their history of development in terms of the software metrics and potentially modify our hype cycle into a more precise graph that would show community’s interest to open-source software projects and repositories.
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ls-type:: annotation
|
||
hl-page:: 10
|
||
hl-color:: green
|
||
id:: 6361ae96-f971-49af-8771-5050da3abdf2
|
||
- To validate our clustering, we manually labeled the ”active”133 repositories according to our identified clusters using expert judgement. Then we took the manually labeled (according to the subclusters c21 – c26) 133 repositories (from the cluster c2) and automatically labeled 1526 repositories (from the cluster c1) and reconstructed the initial dataset with 1659 repositories, however now with labels. We used these labels to conduct the cross-validation using random forest classifier(since it lowers risk of overfitting) [30] and to estimate the accuracy of the classification. The achieved mean accuracy with shuffling and splitting 10 times and train to test ratio being 70/30 is 0.92 with standard deviation of 0.011.
|
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ls-type:: annotation
|
||
hl-page:: 8
|
||
hl-color:: purple
|
||
id:: 6361aeb2-a99c-4108-a66b-fae86b3ae9bd |