How

Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Yuyang [verfasserIn] Noei, Ehsan [verfasserIn] Lyons, Kelly [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Empirical study Software popularity Clustering

Übergeordnetes Werk:	Enthalten in: Information & software technology - Amsterdam [u.a.] : Elsevier Science, 1987, 148
Übergeordnetes Werk:	volume:148

DOI / URN:	10.1016/j.infsof.2022.106924

Katalog-ID:	ELV007903707

Internformat


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100	1		\|a Liu, Yuyang \|e verfasserin \|0 (orcid)0000-0003-2602-6236 \|4 aut
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520			\|a Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity.
650		4	\|a Empirical study
650		4	\|a Software popularity
650		4	\|a Clustering
700	1		\|a Noei, Ehsan \|e verfasserin \|0 (orcid)0000-0001-7192-4604 \|4 aut
700	1		\|a Lyons, Kelly \|e verfasserin \|0 (orcid)0000-0001-6866-7301 \|4 aut
773	0	8	\|i Enthalten in \|t Information & software technology \|d Amsterdam [u.a.] : Elsevier Science, 1987 \|g 148 \|h Online-Ressource \|w (DE-627)320419185 \|w (DE-600)2002332-7 \|w (DE-576)259271160 \|7 nnns
773	1	8	\|g volume:148
912			\|a GBV_USEFLAG_U
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912			\|a GBV_ILN_69
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912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 85.00 \|j Betriebswirtschaft: Allgemeines
936	b	k	\|a 54.50 \|j Programmierung: Allgemeines
951			\|a AR
952			\|d 148

Indexfelder

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matchkey_str	ELV007903707
hierarchy_sort_str	2022
bklnumber	85.00 54.50
publishDate	2022
allfields	10.1016/j.infsof.2022.106924 doi (DE-627)ELV007903707 (ELSEVIER)S0950-5849(22)00077-5 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Liu, Yuyang verfasserin (orcid)0000-0003-2602-6236 aut How 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity. Empirical study Software popularity Clustering Noei, Ehsan verfasserin (orcid)0000-0001-7192-4604 aut Lyons, Kelly verfasserin (orcid)0000-0001-6866-7301 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 148 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 148
spelling	10.1016/j.infsof.2022.106924 doi (DE-627)ELV007903707 (ELSEVIER)S0950-5849(22)00077-5 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Liu, Yuyang verfasserin (orcid)0000-0003-2602-6236 aut How 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity. Empirical study Software popularity Clustering Noei, Ehsan verfasserin (orcid)0000-0001-7192-4604 aut Lyons, Kelly verfasserin (orcid)0000-0001-6866-7301 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 148 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 148
allfields_unstemmed	10.1016/j.infsof.2022.106924 doi (DE-627)ELV007903707 (ELSEVIER)S0950-5849(22)00077-5 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Liu, Yuyang verfasserin (orcid)0000-0003-2602-6236 aut How 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity. Empirical study Software popularity Clustering Noei, Ehsan verfasserin (orcid)0000-0001-7192-4604 aut Lyons, Kelly verfasserin (orcid)0000-0001-6866-7301 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 148 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 148
allfieldsGer	10.1016/j.infsof.2022.106924 doi (DE-627)ELV007903707 (ELSEVIER)S0950-5849(22)00077-5 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Liu, Yuyang verfasserin (orcid)0000-0003-2602-6236 aut How 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity. Empirical study Software popularity Clustering Noei, Ehsan verfasserin (orcid)0000-0001-7192-4604 aut Lyons, Kelly verfasserin (orcid)0000-0001-6866-7301 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 148 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 148
allfieldsSound	10.1016/j.infsof.2022.106924 doi (DE-627)ELV007903707 (ELSEVIER)S0950-5849(22)00077-5 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Liu, Yuyang verfasserin (orcid)0000-0003-2602-6236 aut How 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity. Empirical study Software popularity Clustering Noei, Ehsan verfasserin (orcid)0000-0001-7192-4604 aut Lyons, Kelly verfasserin (orcid)0000-0001-6866-7301 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 148 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 148
language	English
source	Enthalten in Information & software technology 148 volume:148
sourceStr	Enthalten in Information & software technology 148 volume:148
format_phy_str_mv	Article
bklname	Betriebswirtschaft: Allgemeines Programmierung: Allgemeines
institution	findex.gbv.de
topic_facet	Empirical study Software popularity Clustering
dewey-raw	330
isfreeaccess_bool	false
container_title	Information & software technology
authorswithroles_txt_mv	Liu, Yuyang @@aut@@ Noei, Ehsan @@aut@@ Lyons, Kelly @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	320419185
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id	ELV007903707
language_de	englisch
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Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. 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title	How
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abstract	Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity.
abstractGer	Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity.
abstract_unstemmed	Context:: Recent studies on open source platforms, such as GitHub, provide insights into how developers engage with software artifacts such as ReadMe files. Since ReadMe files are usually the first item users interact with in a repository, it is important that ReadMe files provide users with the information needed to engage with the corresponding repository.Objective:: We investigate and compare ReadMe files of open source Java projects on GitHub in order to (i) determine the degree to which ReadMe files are aligned with the official guidelines, (ii) identify the common patterns in the structure of ReadMe files, and (iii) characterize the relationship between ReadMe file structure and popularity of associated repositories.Method:: We apply statistical analyzes and clustering methods on 14,901 Java repositories to identify structural patterns of ReadMe files and the relationship of ReadMe file structure to repository stars.Results:: While the majority of ReadMe files do not align with the GitHub guidelines, repositories whose ReadMe files follow the GitHub guidelines tend to receive more stars. We identify 32 clusters of common ReadMe file structures and the features associated with each structure. We show that projects with ReadMe files that contain project name, usage information, installation instructions, license information, code snippets, or links to images tend to get more stars.Conclusion:: ReadMe file structure shares a statistically significant relationship with popularity as measured by number of stars; however, the most frequent ReadMe file structures are associated with less popular repositories on GitHub. Our findings can be used to understand the importance of ReadMe file structures and their relationship with popularity.
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title_short	How
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up_date	2024-07-06T17:51:59.583Z
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score	7.4013834

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