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c6e1e2b00feca97b662fe67af434f090df3fc339 | An Exploratory Study of Bot Commits | 2,020 | 21 | [
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"[16] categorized the Bot Commits by the type of change (files added, deleted, or modified), find the more commonly"
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c6e1e2b00feca97b662fe67af434f090df3fc339 | An Exploratory Study of Bot Commits | 2,020 | 21 | [
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"name": "Bogdan Vasilescu"
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"It is also known that bots perform 12 different tasks in a repository such as verifying license agreement, code review, dependency checks, merging PR, etc. [22, 7]."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"We also report the AUC (Area Under the ROC curve [55]), which is independent from the class and globally evaluates the worth of the classifier: An AUC of 0.5 indicates a model that has no capability of distinguishing between the two classes."
] | [] | true |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"…the WoC data are curated to identify and cross-link forks [73] and different aliases corresponding to unique author IDs [37], and include information on all versions of the code (including READMEs), commit activity timelines, and time-stamped package dependencies, which are needed for our analysis."
] | [] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"In particular, WoC’s ability to cross-reference and track the history of code versions across nearly all public repositories, along with its curated data that addresses complex challenges like repository defork-ing [18] and author ID aliasing [19], makes this approach feasible."
] | [] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"Furthermore, as Robles et al. [22] discovered, integrating data from heterogeneous sources makes identity disambiguation even more difficult [23], [24]."
] | [] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"By making the dataset openly available, we aim to foster new research in software engineering and contribute to better practices in the OSS ecosystem."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"For example, Fry et al. [2020] share an SED on which they performed identity resolution via commit author identifiers.",
"…VulinOSS [Gkortzis et al., 2018] 8 human factors CROP [Paixão et al., 2018], Enterprise-Driven [Spinellis et al., 2020b], Identity Resolution [Fry et al., 2020], Linux Kernel [Xu and Zhou, 2... | [] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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}
] | ca8fdc3dc23b403277188c526a882dac145448a1 | [
"We leverage previous work on string matching author names [13, 19] and computed the jaro-winkler similarity between the author names.",
"To mitigate this threat, we leverage previous work [13, 19] and compute the jaro-winkler similarity between author names."
] | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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] | f441d9af2c23d2864e9cbfbd6a69e9cdb4d07747 | [
"Fry et al. [33] for details on the random forest model used to merge developer aliases based on their user IDs."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"To resolve authors who have multiple email addresses (aliased authors) we adopt the methodology of Tanner et al. [7], which maps author identity from similar author emails to author names.",
"Tanner et al. [7] noted that authors could have multiple identities, and could triage bug reports to themselves."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"On average, each repository had 81 commits and three authors—although the number of authors per repository can be misleading, as the same person can commit to a repository using different authorship information, a problem that has been identified before [17] and was confirmed by us through manual inspection."
] | [
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] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
{
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] | d9ef2311bfcc8028f96da426a5cf78cf5e0c1a84 | [
"To merge different version control system (VCS) aliases of the same contributors we use the author deduplication maps provided by WoC, the construction of which is explained in detail in the work of Fry et al. [25]."
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"methodology"
] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
{
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"…a developer who is a participant of the hackathon project (thus, they are creating the code/reusing what they had\n7We used the approach outlined by Fry et al. (2020) for author ID disambiguation to merge all of the different IDs belonging to one developer together, which is a common occurrence,…"
] | [] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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] | bbb1824f51a7291e9f2f0e43957e6d7fe50af253 | [
"address approaches [51], which rather have the benefit that they scale to larger datasets."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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] | e230ded658393b34ea2d844d60d385f50ee14632 | [
"There are many potential ways to disambiguate the identities of contributors 12,13 , each presenting tradeoffs.",
"It considers and handles multiple issues common to the study of collaborative software development data 11 : contributor disambiguation 12,13 , bot detection 14 , and the identification of nested pro... | [
"background"
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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] | db5c5f69cbf840a1b70087a17ea1f2d89c481a38 | [
"We have also looked at the number of contributing authors in each project using WoC project to author mappings (P2A) which maps the deforked projects to aliased author IDs [13].",
"looked at the number of contributing authors in each project using WoC project to author mappings (P2A) which maps the deforked proj... | [
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] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
{
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] | b31dab638c3150593bd4c09db939d5e567c00d8e | [
"Dealing with duplicate or aliased entities from disparate sources of data has been discussed in literature [7, 9, 33]."
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] | false |
c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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"One paper did discuss ethics issues and described how the data was pseudonymised (Fry et al. 2020)."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
{
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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] | 6e1c2ed0d4c3ea5780e9c295a60b1814791336e0 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
{
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] | 50a8982971266f850c87bf6cf65b10fdce0ed870 | [
", GitHub logins) to identify users rather than git logs [38, 39] or mailing lists [40] where aliases are poorly resolved."
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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c8d40343dfc0654154e42717209e1d6ab70ebc01 | A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits | 2,020 | 37 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
{
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
{
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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"[15] proposed Skill Space to conceptualize domain expertise."
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
{
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] | abe78288366f7b38149ca0853daa65a4d1b71214 | [
"on the PR acceptance; [7], which showed that the specific expertise of developers might influence PR acceptance probability; [30], which analyzed the association of various technical and social measures, e."
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
{
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] | fccc28ee39056f1cb8b171b62a909de2fd1dae81 | [
"…[7] which assume that similar developers may prefer similar tasks; 3) the content-based approaches [8], [9] and the representation-based approaches [10], [11] usually focus on measuring the content (or text) relevance or learning the vector representation (aka. embedding) for both tasks and…"
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
{
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] | b49b05fe038b8cb31203f0dbe1c774031a53b6d2 | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
{
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] | 334bec97a3a90239030b0864facc991324b86b6a | [
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d558e0171d1cb60071ec5d55c222823005141067 | Representation of Developer Expertise in Open Source Software | 2,020 | 30 | [
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] | 7afa8f00f1b2fa79bdf26ef4bf6cab56b2ad028a | [
"Building and maintaining a skilled workforce capable of conducting effective penetration testing for embedded systems is a significant challenge [22]."
] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 5997beadd8826acbea5d4c7057705b7d1bbfcc67 | [
"Defect Prediction Software defect analysis techniques seek to detect [23]–[28] or predict [29]–[32] bugs in software."
] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 46ffad6efd5f2dab609ad7ee78c902b3d20f4b58 | [
"Related to this work is the field of software defect detection, which seeks to characterize [24, 31, 36, 37, 40, 49, 50, 83] and discover [44, 73, 76] flaws in software, i.e. bugs.",
"Additionally, Dey et al. [24] propose a usage-independent software quality metric, defined as the total quantity of GitHub issues... | [
"methodology",
"background"
] | true |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 5d37df4c75cf213999bbd097e843ff3163b5596a | [
"We use random forest because it is known to offer a good balance between performance and interpretability and is commonly used in software engineering research [15], [43]."
] | [
"methodology"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
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{
"authorId": "1702551",
"name": "A. Mockus"
}
] | b7bb2b2762a0291d42928b1b9b8f8a5a34259337 | [] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | c736a6117829b3a3d284895a6c00294a7a56af2b | [
"Many applications are stuck on errors and not recovered and restored to their present state, and the users are losing their work and facing many issues [2]."
] | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | dc8a53a9117bb7a97519ad9ccbb657236da96e79 | [
"by looking at the associated CTAG tokens - a dataset available in World of Code), identifying other factors that affect code reuse, including code quality [10, 12, 13], project popularity [15], the developers’ mastery on the project topics [19], the supply chain of a particular software [2, 11] etc.",
"…populari... | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | a3523fb6251b0046f7f9008941b1077d8d8f44b6 | [] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 5a1da8a92a525c0c994e1193fcf84bb2c90cb36d | [] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 56f1ce9f5d440fc7fcdca4517422aa31a4e226bf | [
"by looking at the associated CTAG tokens - a dataset available in WORLD OF CODE), identifying other factors that affect code reuse, including code quality [14], [15], project popularity [16], [17], the type of Open Source license used, etc."
] | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | c5a850f50a95a56cf7000425c8bb59e87f80d6ee | [
"by looking at the associated CTAG tokens - a dataset available in WORLD OF CODE), identifying other factors that affect code reuse, including code quality [51], [52], project popularity [53], [54], the type of Open Source license used, etc."
] | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | a24b7c0d12e86099e5a11164609c92b030d129a5 | [
"Bayesian networks [60] are arguably the best-known kind of graphical models, which can be used both directly as probabilistic classifiers [17; 41] but have also become the basis to encode causal relations that go beyond mere correlations [49]."
] | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 12404a5147b39e9a34849606bf2c056f84903c8d | [] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 4dbbfcf0f9014bb4cbcc0b4f15027d835bbd6fff | [
"We used the Random Forest regression method as it works well with almost all types of data, generally does not overfit, and it is easy to get the relative importance of the predictor variables from a trained model [67]."
] | [
"methodology"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | abe78288366f7b38149ca0853daa65a4d1b71214 | [
"the dependency networks in NPM [6], NPM package popularity [9], issues raised against the packages [8, 10, 12], problems associated with library migration [41] etc."
] | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | d558e0171d1cb60071ec5d55c222823005141067 | [
"sforidentityresolution(similarto [46]);c) analyzing the skill vectors of the developers in a project to infer the transparency of the corresponding software supply chain [50], [51], [52], [53], [54], [55]. VIII. CONCLUSION We have established a proof-of-concept for Skill Space: an approach to represent packages (A... | [
"methodology"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 91ccab6104c8f7be62ffe8dc992bcac54f96b0eb | [
"To learn the BN structure from our data, we chose a well-performing and widely used (Dey and Mockus 2020) Hill-Climbing (HC) algorithm from the bnlearn R package.",
"ge [1] as additional evidence for the choice of our discretization levels. 4.1.2 BN Structure Search: Hill Climbing To learn the BN structure from ... | [
"methodology"
] | true |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | c8d40343dfc0654154e42717209e1d6ab70ebc01 | [
"pen Source Software [17], and/or link it to mailing lists [16]. Knowing the actual number of developers who contribute to or use a project can also influence the calculations of the number of defects [5, 7], or the popularity of the project [6]. The most accurate methods for identifying connected author IDs, like ... | [
"methodology"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | c6e1e2b00feca97b662fe67af434f090df3fc339 | [
"[11], and also investigate how the presence of bots affect the popularity Dey and Mockus [4] of a software and, in turn, affect the number of issues Dey and Mockus [5, 6] and pull request acceptance Dey and Mockus [7].",
"KEYWORDS\nBots, Automated Commits, Code Commits, social coding platforms\nACM Reference For... | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | a743e4b5cafbe988a02be3f981e2724963ee4ef2 | [
"Many software researchers look at the activity of software developers for understanding their cultural behavior [3, 13–17], estimating team size [7], measuring productivity [49], and studying developer interaction such as knowledge flow within a project [32, 36] and prediction of build failures [48]."
] | [
"background"
] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | c7e5ab1c3221bf73b2a6e0ea97d31794dece57b1 | [] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 55ee2245a2352ee9c77238271cd71971fbcdfc9b | [] | [] | false |
decc56122a977d1b8071dd325f0f990cd4557d6b | Deriving a usage-independent software quality metric | 2,020 | 22 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | b3e8ccd3c8952f2a71dd8ba7355d33ebc368ab15 | [
"Investigating the characteristics and impacts of open-source package ecosystems forms a large and active body of research [3, 7, 15, 32, 33, 36, 37]."
] | [] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | be466dd494d9ba55e3f5b86f5542704aa474bab5 | [
"Research [13, 14, 24, 27, 48, 60] has consistently shown that building trust between developers and open-source repository maintainers is crucial for successful contributions to open-source software."
] | [] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 98553bf38dced2502a27be7a43baa3fc5dd3ab1b | [
"Subsequently, we further elaborate on how we construct the repository dataset randomly, referring to works [8], [48]."
] | [
"methodology"
] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 137ad9def2c81ff45849550a15c9e7af5a7bd5cf | [
"These models were selected due to their role as the underlying techniques for existing PR outcome predictive models, namely CTCPPre [28] and RForest PR [27], which we introduced for comparative analysis in Section II.",
"Additionally, they have delved into the code review process, participation factors, and pred... | [
"methodology",
"background"
] | true |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 08bf0d73fddebe92fe5ed5bcfaf8b384ca725ebc | [
"…of the time required to close PRs [3], [5], investigating social aspects of PRs acceptance [11], investigating the personality traits of developers involved in the PRs acceptance process [12], or looking at the characteristics of PRs that increase the acceptance rate of PRs [13], [14], [15].",
"The acceptance o... | [
"result",
"background"
] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | b7bb2b2762a0291d42928b1b9b8f8a5a34259337 | [] | [] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | b427620250eab16fb23f13de88bb4572d8bd63af | [
"In 2020, Dey et al. [16] collected 50 factors of 483,988 pull requests based on 4,218 projects.",
"These factors also include factors that focus only on specific scenarios, e.g., factors related to Microsoft [3] and npm ecosystems only [16].",
"A more recent study by Dey et al. [16] combined many such factors (... | [
"background"
] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | 8963e7aec92235d3c02df463f40341a35f64ff4f | [
"Since then, many studies have been conducted on investigating the effects of socio-technical factors on pull request quality in an OSS development environment [16, 17, 57]."
] | [
"background"
] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | ba3fb66935059705953e5e70c669dae06101269a | [
"7 hours) for NPM packages on GitHub [14].",
"This value is slightly lower than that of a recent study on NPM packages by Dey and Mockus [14], who reported a PR acceptance rate of 60%.",
"The time taken to merge those pull requests is about four times faster than for NPM packages on GitHub [14] and 24 times fas... | [
"result",
"methodology",
"background"
] | false |
e9fff6449a13f2c9ce548cab4cc0701f7466c77a | Which Pull Requests Get Accepted and Why? A study of popular NPM Packages | 2,020 | 15 | [
{
"authorId": "8041820",
"name": "Tapajit Dey"
},
{
"authorId": "1702551",
"name": "A. Mockus"
}
] | ca16ab513fa6299e1024ba6d9780254fe1118209 | [
"This also include factors that only focuses on specific scenarios, e.g., factors related to Microsoft [3], NPM ecosystem only [16], etc.",
"A more recent study by Dey et al. [16] combined many such factors (50) to rank their importance for prediction.",
"In 2020, Dey et al. [16] collected 50 factors of 483,988 ... | [
"background"
] | false |
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