logseq/pages/hls__icse2023-paper223_1666614236200_0.md

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• technical debt prediction ls-type:: annotation hl-page:: 1 hl-color:: yellow id:: 6356cb9b-96f6-43e9-aff8-2a144ff92bd7
• consensus clustering approac ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 6356cbc0-068c-400c-b1cd-e2f3cf1333aa hl-stamp:: 1666632648322
• It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner’s hype cycle ls-type:: annotation hl-page:: 1 hl-color:: yellow id:: 6356cbee-4989-4235-80dc-00e293e40bfe
• n this work, we aim to overcome the limitations (such as limited validity) of using a single clustering method with our main objectives along with key aspects of originality being the following: ls-type:: annotation hl-page:: 1 hl-color:: yellow id:: 6356cce6-4135-4399-bf28-03118e86e9ae
• onsensus clusterin ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 6356cd24-872f-41d9-9ecc-dafb1860b57b hl-stamp:: 1666633011154
• multiple cluster validity indices ls-type:: annotation hl-page:: 1 hl-color:: purple id:: 6356cd3a-d252-42a7-bb63-a8c7603f2886
• The relevance of this study can be mostly attributed to studying the software ecosystems and identifying important indicators about development activity in software projects ls-type:: annotation hl-page:: 2 hl-color:: yellow id:: 6356cd98-c4af-44f7-9b3f-0ac16e70e4a2
• prototype” vectors ls-type:: annotation hl-page:: 2 hl-color:: purple id:: 6356cdd5-9133-44a6-89f3-8c55aaa20ae1
• analyze the results ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356ce1a-9ec6-4a49-9391-a217d12baf56 hl-stamp:: 1666633245706
• details significant parts of the methodology such as clustering with application of validity indices, consensus clustering, and aggregation of the results ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356ce2b-6c3d-4184-80fb-d370b96c1a8e
• web libraries and frameworks ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356cf26-2514-416b-a73c-08abe6018aad
• non-web libraries and frameworks ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356cf31-580a-45aa-aeb3-7e76a875a96c
• software tools ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356cf50-79ae-4bda-b29f-a7d9dfe0d678
• Commit-related event ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356cfe8-55f0-40a6-9066-3f816a9237df
• Issue-related even ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356cff2-464f-4716-a566-62c345bc7ee6
• Pull request-related even ls-type:: annotation hl-page:: 2 hl-color:: green id:: 6356cffa-43e0-42b2-a11e-a004d5744905
• ] , ls-type:: annotation hl-page:: 2 hl-color:: red id:: 6356d017-7e72-4e11-956f-35aaeb5eda5e
• , ls-type:: annotation hl-page:: 3 hl-color:: red id:: 6356d032-b692-475a-b46c-3577fa01e677
• we try to identify the metrics that would characterize software repositories qualitatively and signal about potential anomalies ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6356d03c-785d-4fca-adf3-93fdc6881e0b
• “value simple and easily understandable metrics over complex ones ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6356d087-1588-4870-823e-24378961c127
• 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. ls-type:: annotation hl-page:: 3 hl-color:: yellow id:: 6356d0ca-0058-46ec-8e80-c7ddd3269913
• 2.1. ls-type:: annotation hl-page:: 3 hl-color:: red id:: 6356d0ec-71b7-4970-8f96-c0f5157c57fb
• The limitation of such clustering is that it produces too element-specific, not descriptive, and small clusters. ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6356d12d-2c44-4132-9390-bbcf9ece62ca
• two cluster ls-type:: annotation hl-page:: 3 hl-color:: yellow id:: 6356d170-ce29-41c0-8190-f09d18bc968c hl-stamp:: 1666634101493
• Tsoukalas et al. (2021) [10] divided 27 software projects from the technical debt dataset [11] into six clusters of similar projects with respect to their technical debt aspects using Kmeans algorithm and built specific technical debt forecasting models for each cluster using regression algorithms. ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6356d26a-d805-47a2-b4a7-9197fee85841
• The results showed that the prediction errors tend to be statistically significantly lower in within-cluster technical debt forecasting than in cross-cluster forecastin ls-type:: annotation hl-page:: 3 hl-color:: green id:: 6356d4b0-eb9c-475e-8850-b54692ea0bef
• In this work, we try to overcome the limitation of cluster validity by using multiple clustering techniques, cluster validity indices, and consensus clusterin ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 6356d572-7838-40b2-9ebb-20965c0d258c
• onsensus clustering to analyze software repository metric ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 6356d5dd-19ba-49bc-b071-6caa9796a72c
• ize or popularity based clustering ls-type:: annotation hl-page:: 4 hl-color:: green id:: 6356d72b-4f96-412b-b62c-99b47ed1280e
• data collection and preparation ls-type:: annotation hl-page:: 4 hl-color:: green id:: 6356d7a9-213a-49ca-9be7-ec4dec1adaa4
• redefine the fixed number of consensus clusters ls-type:: annotation hl-page:: 4 hl-color:: green id:: 6356d7ba-ee81-4511-b1da-2637eef1f5d7
• We normalize each metric to the [0,1] range. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 6356d7ec-cdc7-4fae-ae05-b915bdbb8a32
• m clustering algorithms with different values of parameters that we tune. ls-type:: annotation hl-page:: 4 hl-color:: green id:: 6356d7f6-93be-4ce5-9583-27e270f6ff4a
• three cluster validity indices ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 6356d806-f610-4275-8e96-1f5862b94d31
• winner” ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 6356d870-8683-46aa-8db3-3a471d7804d9
• we get a collection of c sets of prototype vectors ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 6356d8af-e6d2-4a51-8426-d9c284b7b72c
• The final step of our methodology is aggregation of the results. We match the calculated cluster prototypes to compare them and the clusters they represent across P sets of clustered repositories. For that, we consider the minimum-weight matching problem in P -partite graph across all c · P prototypes (for details refer to Section III-D), and, as a result, get c sets of P matched prototypes. We calculate the discrepancy in every such set of prototype vectors and, using a similarity measure, calculate the discrepancy value d for each of the c sets, which shows us how different the cluster prototypes are across the P sets of data for each of the c clusters. ls-type:: annotation hl-page:: 4 hl-color:: yellow id:: 6356d962-696b-481d-b08c-3098461da7f7
• if two out of three indices showed the same solution, and the third one showed a new solution, we would consider the two unique solution ls-type:: annotation hl-page:: 5 hl-color:: green id:: 6356d9e3-5da3-4fc8-bd09-91a14d5d5e37
• if all three showed the same solution or different solutions, we would consider only one or three unique solutions correspondingly ls-type:: annotation hl-page:: 5 hl-color:: green id:: 6356d9f2-adb2-49ec-8718-0396690a8ff9
• Silhouette, Calinski-Harabasz, and DaviesBouldin ls-type:: annotation hl-page:: 5 hl-color:: green id:: 6356dab9-6876-4531-8cb4-1709cb6f1160
• mean nearest-cluster distanc ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 6356e087-e141-4bc5-8e1a-4fa042d7e72a
• ntra-cluster distance ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 6356e08b-5698-4395-9a70-46d162b6423d
• silhouette coefficient ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 6356e303-65ce-48a3-abe4-4f86b2978aa0
• Cohesion is estimated based on the distances from the repositories in a cluster to its cluster centroid and separation is based on the distance of the cluster centroids from the global centroid ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 6356e43d-fe11-4b65-a710-80a46abb4a00
• cluster ensemble ls-type:: annotation hl-page:: 5 hl-color:: yellow id:: 6356e4e1-85db-4df1-9014-cc8cdbe7dbd8
• quare matrix S ls-type:: annotation hl-page:: 5 hl-color:: green id:: 6356e5af-06b1-4057-9dc4-962699459775
• Meta-clustering algorithm is based on clustering clusters of software repositorie ls-type:: annotation hl-page:: 6 hl-color:: yellow id:: 6356e5f8-1a4a-442c-9fa4-fc3ec528a7ee