A fine-grained data set and analysis of tangling in bug fixing commits
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the pre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Herbold, Steffen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Empirical software engineering - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996, 27(2022), 6 vom: 02. Juli |
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| Übergeordnetes Werk: |
volume:27 ; year:2022 ; number:6 ; day:02 ; month:07 |
| Links: |
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| DOI / URN: |
10.1007/s10664-021-10083-5 |
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| Katalog-ID: |
SPR047480971 |
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| 041 | |a eng | ||
| 100 | 1 | |a Herbold, Steffen |e verfasserin |0 (orcid)0000-0001-9765-2803 |4 aut | |
| 245 | 1 | 2 | |a A fine-grained data set and analysis of tangling in bug fixing commits |
| 264 | 1 | |c 2022 | |
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| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s) 2022 | ||
| 520 | |a Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. | ||
| 650 | 4 | |a Tangled changes |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tangled commits |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bug fix |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Manual validation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Research turk |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Registered report |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Trautsch, Alexander |4 aut | |
| 700 | 1 | |a Ledel, Benjamin |4 aut | |
| 700 | 1 | |a Aghamohammadi, Alireza |4 aut | |
| 700 | 1 | |a Ghaleb, Taher A. |4 aut | |
| 700 | 1 | |a Chahal, Kuljit Kaur |4 aut | |
| 700 | 1 | |a Bossenmaier, Tim |4 aut | |
| 700 | 1 | |a Nagaria, Bhaveet |4 aut | |
| 700 | 1 | |a Makedonski, Philip |4 aut | |
| 700 | 1 | |a Ahmadabadi, Matin Nili |4 aut | |
| 700 | 1 | |a Szabados, Kristof |4 aut | |
| 700 | 1 | |a Spieker, Helge |4 aut | |
| 700 | 1 | |a Madeja, Matej |4 aut | |
| 700 | 1 | |a Hoy, Nathaniel |4 aut | |
| 700 | 1 | |a Lenarduzzi, Valentina |4 aut | |
| 700 | 1 | |a Wang, Shangwen |4 aut | |
| 700 | 1 | |a Rodríguez-Pérez, Gema |4 aut | |
| 700 | 1 | |a Colomo-Palacios, Ricardo |4 aut | |
| 700 | 1 | |a Verdecchia, Roberto |4 aut | |
| 700 | 1 | |a Singh, Paramvir |4 aut | |
| 700 | 1 | |a Qin, Yihao |4 aut | |
| 700 | 1 | |a Chakroborti, Debasish |4 aut | |
| 700 | 1 | |a Davis, Willard |4 aut | |
| 700 | 1 | |a Walunj, Vijay |4 aut | |
| 700 | 1 | |a Wu, Hongjun |4 aut | |
| 700 | 1 | |a Marcilio, Diego |4 aut | |
| 700 | 1 | |a Alam, Omar |4 aut | |
| 700 | 1 | |a Aldaeej, Abdullah |4 aut | |
| 700 | 1 | |a Amit, Idan |4 aut | |
| 700 | 1 | |a Turhan, Burak |4 aut | |
| 700 | 1 | |a Eismann, Simon |4 aut | |
| 700 | 1 | |a Wickert, Anna-Katharina |4 aut | |
| 700 | 1 | |a Malavolta, Ivano |4 aut | |
| 700 | 1 | |a Sulír, Matúš |4 aut | |
| 700 | 1 | |a Fard, Fatemeh |4 aut | |
| 700 | 1 | |a Henley, Austin Z. |4 aut | |
| 700 | 1 | |a Kourtzanidis, Stratos |4 aut | |
| 700 | 1 | |a Tuzun, Eray |4 aut | |
| 700 | 1 | |a Treude, Christoph |4 aut | |
| 700 | 1 | |a Shamasbi, Simin Maleki |4 aut | |
| 700 | 1 | |a Pashchenko, Ivan |4 aut | |
| 700 | 1 | |a Wyrich, Marvin |4 aut | |
| 700 | 1 | |a Davis, James |4 aut | |
| 700 | 1 | |a Serebrenik, Alexander |4 aut | |
| 700 | 1 | |a Albrecht, Ella |4 aut | |
| 700 | 1 | |a Aktas, Ethem Utku |4 aut | |
| 700 | 1 | |a Strüber, Daniel |4 aut | |
| 700 | 1 | |a Erbel, Johannes |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Empirical software engineering |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996 |g 27(2022), 6 vom: 02. Juli |w (DE-627)271350032 |w (DE-600)1479898-0 |x 1573-7616 |7 nnns |
| 773 | 1 | 8 | |g volume:27 |g year:2022 |g number:6 |g day:02 |g month:07 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10664-021-10083-5 |z kostenfrei |3 Volltext |
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10.1007/s10664-021-10083-5 doi (DE-627)SPR047480971 (SPR)s10664-021-10083-5-e DE-627 ger DE-627 rakwb eng Herbold, Steffen verfasserin (orcid)0000-0001-9765-2803 aut A fine-grained data set and analysis of tangling in bug fixing commits 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Tangled changes (dpeaa)DE-He213 Tangled commits (dpeaa)DE-He213 Bug fix (dpeaa)DE-He213 Manual validation (dpeaa)DE-He213 Research turk (dpeaa)DE-He213 Registered report (dpeaa)DE-He213 Trautsch, Alexander aut Ledel, Benjamin aut Aghamohammadi, Alireza aut Ghaleb, Taher A. aut Chahal, Kuljit Kaur aut Bossenmaier, Tim aut Nagaria, Bhaveet aut Makedonski, Philip aut Ahmadabadi, Matin Nili aut Szabados, Kristof aut Spieker, Helge aut Madeja, Matej aut Hoy, Nathaniel aut Lenarduzzi, Valentina aut Wang, Shangwen aut Rodríguez-Pérez, Gema aut Colomo-Palacios, Ricardo aut Verdecchia, Roberto aut Singh, Paramvir aut Qin, Yihao aut Chakroborti, Debasish aut Davis, Willard aut Walunj, Vijay aut Wu, Hongjun aut Marcilio, Diego aut Alam, Omar aut Aldaeej, Abdullah aut Amit, Idan aut Turhan, Burak aut Eismann, Simon aut Wickert, Anna-Katharina aut Malavolta, Ivano aut Sulír, Matúš aut Fard, Fatemeh aut Henley, Austin Z. aut Kourtzanidis, Stratos aut Tuzun, Eray aut Treude, Christoph aut Shamasbi, Simin Maleki aut Pashchenko, Ivan aut Wyrich, Marvin aut Davis, James aut Serebrenik, Alexander aut Albrecht, Ella aut Aktas, Ethem Utku aut Strüber, Daniel aut Erbel, Johannes aut Enthalten in Empirical software engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996 27(2022), 6 vom: 02. Juli (DE-627)271350032 (DE-600)1479898-0 1573-7616 nnns volume:27 year:2022 number:6 day:02 month:07 https://dx.doi.org/10.1007/s10664-021-10083-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 6 02 07 |
| spelling |
10.1007/s10664-021-10083-5 doi (DE-627)SPR047480971 (SPR)s10664-021-10083-5-e DE-627 ger DE-627 rakwb eng Herbold, Steffen verfasserin (orcid)0000-0001-9765-2803 aut A fine-grained data set and analysis of tangling in bug fixing commits 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Tangled changes (dpeaa)DE-He213 Tangled commits (dpeaa)DE-He213 Bug fix (dpeaa)DE-He213 Manual validation (dpeaa)DE-He213 Research turk (dpeaa)DE-He213 Registered report (dpeaa)DE-He213 Trautsch, Alexander aut Ledel, Benjamin aut Aghamohammadi, Alireza aut Ghaleb, Taher A. aut Chahal, Kuljit Kaur aut Bossenmaier, Tim aut Nagaria, Bhaveet aut Makedonski, Philip aut Ahmadabadi, Matin Nili aut Szabados, Kristof aut Spieker, Helge aut Madeja, Matej aut Hoy, Nathaniel aut Lenarduzzi, Valentina aut Wang, Shangwen aut Rodríguez-Pérez, Gema aut Colomo-Palacios, Ricardo aut Verdecchia, Roberto aut Singh, Paramvir aut Qin, Yihao aut Chakroborti, Debasish aut Davis, Willard aut Walunj, Vijay aut Wu, Hongjun aut Marcilio, Diego aut Alam, Omar aut Aldaeej, Abdullah aut Amit, Idan aut Turhan, Burak aut Eismann, Simon aut Wickert, Anna-Katharina aut Malavolta, Ivano aut Sulír, Matúš aut Fard, Fatemeh aut Henley, Austin Z. aut Kourtzanidis, Stratos aut Tuzun, Eray aut Treude, Christoph aut Shamasbi, Simin Maleki aut Pashchenko, Ivan aut Wyrich, Marvin aut Davis, James aut Serebrenik, Alexander aut Albrecht, Ella aut Aktas, Ethem Utku aut Strüber, Daniel aut Erbel, Johannes aut Enthalten in Empirical software engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996 27(2022), 6 vom: 02. Juli (DE-627)271350032 (DE-600)1479898-0 1573-7616 nnns volume:27 year:2022 number:6 day:02 month:07 https://dx.doi.org/10.1007/s10664-021-10083-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 6 02 07 |
| allfields_unstemmed |
10.1007/s10664-021-10083-5 doi (DE-627)SPR047480971 (SPR)s10664-021-10083-5-e DE-627 ger DE-627 rakwb eng Herbold, Steffen verfasserin (orcid)0000-0001-9765-2803 aut A fine-grained data set and analysis of tangling in bug fixing commits 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Tangled changes (dpeaa)DE-He213 Tangled commits (dpeaa)DE-He213 Bug fix (dpeaa)DE-He213 Manual validation (dpeaa)DE-He213 Research turk (dpeaa)DE-He213 Registered report (dpeaa)DE-He213 Trautsch, Alexander aut Ledel, Benjamin aut Aghamohammadi, Alireza aut Ghaleb, Taher A. aut Chahal, Kuljit Kaur aut Bossenmaier, Tim aut Nagaria, Bhaveet aut Makedonski, Philip aut Ahmadabadi, Matin Nili aut Szabados, Kristof aut Spieker, Helge aut Madeja, Matej aut Hoy, Nathaniel aut Lenarduzzi, Valentina aut Wang, Shangwen aut Rodríguez-Pérez, Gema aut Colomo-Palacios, Ricardo aut Verdecchia, Roberto aut Singh, Paramvir aut Qin, Yihao aut Chakroborti, Debasish aut Davis, Willard aut Walunj, Vijay aut Wu, Hongjun aut Marcilio, Diego aut Alam, Omar aut Aldaeej, Abdullah aut Amit, Idan aut Turhan, Burak aut Eismann, Simon aut Wickert, Anna-Katharina aut Malavolta, Ivano aut Sulír, Matúš aut Fard, Fatemeh aut Henley, Austin Z. aut Kourtzanidis, Stratos aut Tuzun, Eray aut Treude, Christoph aut Shamasbi, Simin Maleki aut Pashchenko, Ivan aut Wyrich, Marvin aut Davis, James aut Serebrenik, Alexander aut Albrecht, Ella aut Aktas, Ethem Utku aut Strüber, Daniel aut Erbel, Johannes aut Enthalten in Empirical software engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996 27(2022), 6 vom: 02. Juli (DE-627)271350032 (DE-600)1479898-0 1573-7616 nnns volume:27 year:2022 number:6 day:02 month:07 https://dx.doi.org/10.1007/s10664-021-10083-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 6 02 07 |
| allfieldsGer |
10.1007/s10664-021-10083-5 doi (DE-627)SPR047480971 (SPR)s10664-021-10083-5-e DE-627 ger DE-627 rakwb eng Herbold, Steffen verfasserin (orcid)0000-0001-9765-2803 aut A fine-grained data set and analysis of tangling in bug fixing commits 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Tangled changes (dpeaa)DE-He213 Tangled commits (dpeaa)DE-He213 Bug fix (dpeaa)DE-He213 Manual validation (dpeaa)DE-He213 Research turk (dpeaa)DE-He213 Registered report (dpeaa)DE-He213 Trautsch, Alexander aut Ledel, Benjamin aut Aghamohammadi, Alireza aut Ghaleb, Taher A. aut Chahal, Kuljit Kaur aut Bossenmaier, Tim aut Nagaria, Bhaveet aut Makedonski, Philip aut Ahmadabadi, Matin Nili aut Szabados, Kristof aut Spieker, Helge aut Madeja, Matej aut Hoy, Nathaniel aut Lenarduzzi, Valentina aut Wang, Shangwen aut Rodríguez-Pérez, Gema aut Colomo-Palacios, Ricardo aut Verdecchia, Roberto aut Singh, Paramvir aut Qin, Yihao aut Chakroborti, Debasish aut Davis, Willard aut Walunj, Vijay aut Wu, Hongjun aut Marcilio, Diego aut Alam, Omar aut Aldaeej, Abdullah aut Amit, Idan aut Turhan, Burak aut Eismann, Simon aut Wickert, Anna-Katharina aut Malavolta, Ivano aut Sulír, Matúš aut Fard, Fatemeh aut Henley, Austin Z. aut Kourtzanidis, Stratos aut Tuzun, Eray aut Treude, Christoph aut Shamasbi, Simin Maleki aut Pashchenko, Ivan aut Wyrich, Marvin aut Davis, James aut Serebrenik, Alexander aut Albrecht, Ella aut Aktas, Ethem Utku aut Strüber, Daniel aut Erbel, Johannes aut Enthalten in Empirical software engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996 27(2022), 6 vom: 02. Juli (DE-627)271350032 (DE-600)1479898-0 1573-7616 nnns volume:27 year:2022 number:6 day:02 month:07 https://dx.doi.org/10.1007/s10664-021-10083-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 6 02 07 |
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10.1007/s10664-021-10083-5 doi (DE-627)SPR047480971 (SPR)s10664-021-10083-5-e DE-627 ger DE-627 rakwb eng Herbold, Steffen verfasserin (orcid)0000-0001-9765-2803 aut A fine-grained data set and analysis of tangling in bug fixing commits 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Tangled changes (dpeaa)DE-He213 Tangled commits (dpeaa)DE-He213 Bug fix (dpeaa)DE-He213 Manual validation (dpeaa)DE-He213 Research turk (dpeaa)DE-He213 Registered report (dpeaa)DE-He213 Trautsch, Alexander aut Ledel, Benjamin aut Aghamohammadi, Alireza aut Ghaleb, Taher A. aut Chahal, Kuljit Kaur aut Bossenmaier, Tim aut Nagaria, Bhaveet aut Makedonski, Philip aut Ahmadabadi, Matin Nili aut Szabados, Kristof aut Spieker, Helge aut Madeja, Matej aut Hoy, Nathaniel aut Lenarduzzi, Valentina aut Wang, Shangwen aut Rodríguez-Pérez, Gema aut Colomo-Palacios, Ricardo aut Verdecchia, Roberto aut Singh, Paramvir aut Qin, Yihao aut Chakroborti, Debasish aut Davis, Willard aut Walunj, Vijay aut Wu, Hongjun aut Marcilio, Diego aut Alam, Omar aut Aldaeej, Abdullah aut Amit, Idan aut Turhan, Burak aut Eismann, Simon aut Wickert, Anna-Katharina aut Malavolta, Ivano aut Sulír, Matúš aut Fard, Fatemeh aut Henley, Austin Z. aut Kourtzanidis, Stratos aut Tuzun, Eray aut Treude, Christoph aut Shamasbi, Simin Maleki aut Pashchenko, Ivan aut Wyrich, Marvin aut Davis, James aut Serebrenik, Alexander aut Albrecht, Ella aut Aktas, Ethem Utku aut Strüber, Daniel aut Erbel, Johannes aut Enthalten in Empirical software engineering Dordrecht [u.a.] : Springer Science + Business Media B.V, 1996 27(2022), 6 vom: 02. Juli (DE-627)271350032 (DE-600)1479898-0 1573-7616 nnns volume:27 year:2022 number:6 day:02 month:07 https://dx.doi.org/10.1007/s10664-021-10083-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 6 02 07 |
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Herbold, Steffen @@aut@@ Trautsch, Alexander @@aut@@ Ledel, Benjamin @@aut@@ Aghamohammadi, Alireza @@aut@@ Ghaleb, Taher A. @@aut@@ Chahal, Kuljit Kaur @@aut@@ Bossenmaier, Tim @@aut@@ Nagaria, Bhaveet @@aut@@ Makedonski, Philip @@aut@@ Ahmadabadi, Matin Nili @@aut@@ Szabados, Kristof @@aut@@ Spieker, Helge @@aut@@ Madeja, Matej @@aut@@ Hoy, Nathaniel @@aut@@ Lenarduzzi, Valentina @@aut@@ Wang, Shangwen @@aut@@ Rodríguez-Pérez, Gema @@aut@@ Colomo-Palacios, Ricardo @@aut@@ Verdecchia, Roberto @@aut@@ Singh, Paramvir @@aut@@ Qin, Yihao @@aut@@ Chakroborti, Debasish @@aut@@ Davis, Willard @@aut@@ Walunj, Vijay @@aut@@ Wu, Hongjun @@aut@@ Marcilio, Diego @@aut@@ Alam, Omar @@aut@@ Aldaeej, Abdullah @@aut@@ Amit, Idan @@aut@@ Turhan, Burak @@aut@@ Eismann, Simon @@aut@@ Wickert, Anna-Katharina @@aut@@ Malavolta, Ivano @@aut@@ Sulír, Matúš @@aut@@ Fard, Fatemeh @@aut@@ Henley, Austin Z. @@aut@@ Kourtzanidis, Stratos @@aut@@ Tuzun, Eray @@aut@@ Treude, Christoph @@aut@@ Shamasbi, Simin Maleki @@aut@@ Pashchenko, Ivan @@aut@@ Wyrich, Marvin @@aut@@ Davis, James @@aut@@ Serebrenik, Alexander @@aut@@ Albrecht, Ella @@aut@@ Aktas, Ethem Utku @@aut@@ Strüber, Daniel @@aut@@ Erbel, Johannes @@aut@@ |
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Herbold, Steffen |
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Herbold, Steffen Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher A. Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes |
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fine-grained data set and analysis of tangling in bug fixing commits |
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A fine-grained data set and analysis of tangling in bug fixing commits |
| abstract |
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. © The Author(s) 2022 |
| abstractGer |
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. © The Author(s) 2022 |
| abstract_unstemmed |
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. © The Author(s) 2022 |
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A fine-grained data set and analysis of tangling in bug fixing commits |
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https://dx.doi.org/10.1007/s10664-021-10083-5 |
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Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher A. Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes |
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Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher A. Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes |
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| doi_str |
10.1007/s10664-021-10083-5 |
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2024-07-04T03:16:05.444Z |
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| score |
7.401681 |