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• Al-Subaihin A A, Sarro F, Black S, Capra L, Harman M (2021) App store effects on software engineering practices. Trans Softw Eng 47(2):300–319 DOI: 10.1109/TSE.2019.2891715
• An L, Khomh F, Guéhéneuc Y-G (2018) An empirical study of crash-inducing commits in Mozilla Firefox. Softw Qual J 26:553–584 DOI: 10.1007/s11219-017-9361-y
• Arya D, Wang W, Guo J L C, Cheng J (2019) Analysis and detection of information types of open source software issue discussions. In: International conference on software engineering, pp 454–464
• Avelino G, Passos L, Hora A, Valente M T (2017) Assessing code authorship: The case of the Linux kernel. In: Open source systems: towards robust practices. Springer, pp 151–163
• Bagozzi R P, Yi Y (2012) Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science 40(1):8–34 DOI: 10.1007/s11747-011-0278-x
• Beran T N, Violato C (2010) Structural equation modeling in medical research: a primer. BMC Research Notes 3(1):1–10 DOI: 10.1186/1756-0500-3-267
• Bernard H R, Wutich A, Ryan G W (2016) Analyzing qualitative data: Systematic approaches. SAGE publications, Thousand Oaks
• Bick S, Spohrer K, Hoda R, Scheerer A, Heinzl A (2018) Coordination challenges in large-scale software development: A case study of planning misalignment in hybrid settings. Trans Softw Eng 44(10):932–950 DOI: 10.1109/TSE.2017.2730870
• Bird C (2016) Interviews. In: Perspectives on data science for software engineering. Morgan Kaufmann, Burlington
• Bird C, Gourley A, Devanbu P, Gertz M, Swaminathan A (2006) Mining email social networks. In: International working conference on mining software repositories. ACM, pp 137–143
• Bird C, Zimmermann T (2012) Assessing the value of branches with what-if analysis. In: International symposium on foundations of software engineering. ACM SIGSOFT
• Bogart C, Kästner C, Herbsleb J, Thung F (2021) When and how to make breaking changes: Policies and practices in 18 open source software ecosystems. Trans Softw Eng Methodol, 30(4)
• Borg M, Svensson O, Berg K, Hansson D (2019) SZZ unleashed: An open implementation of the SZZ algorithm. In: MaLTeSQuE. ACM, pp 7–12
• Brooks FP Jr (1974) The mythical man-month. Addison-Wesley Reading, United States
• Campbell J L, Quincy C, Osserman J, Pedersen O K (2013) Coding in-depth semistructured interviews. Sociological Methods & Research 42 (3):294–320 DOI: 10.1177/0049124113500475
• Casalnuovo C, Vasilescu B, Devanbu P, Filkov V (2015) Developer onboarding in GitHub: The role of prior social links and language experience. In: Joint Meeting on ESEC and FSE. ACM, pp 817–828
• Cassee N, Kitsanelis C, Constantinou E, Serebrenik A (2021) Human, bot or both? a study on the capabilities of classification models on mixed accounts. In: 2021 IEEE international conference on software maintenance and evolution (ICSME), pp 654–658
• Cliff N (1993) Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol Bull 114(3):4994–509 DOI: 10.1037/0033-2909.114.3.494
• Costa C, Figueiredo J, Murta L, Sarma A (2016) TIPMerge: recommending experts for integrating changes across branches. In: International symposium on foundations of software engineering, pp 523–534
• Cruzes DS, Dyba T (2011) Recommended steps for thematic synthesis in software engineering. In: International symposium on empirical software engineering and measurement, pp 275–284
• D’Ambros M, Lanza M, Robbes R (2012) Evaluating defect prediction approaches: a benchmark and an extensive comparison. Empir Softw Eng 17(4):531–577 DOI: 10.1007/s10664-011-9173-9
• Datta S (June 2018) How does developer interaction relate to software quality? An examination of product development data. Empir Softw Eng 23 (3):1153–1187
• de Souza Costa C, Figueiredo J J, Pimentel J F, Sarma A, Murta L G P (2019) Recommending participants for collaborative merge sessions. Trans Softw Eng, 1–1
• Dingsøyr T, Moe N B, Fægri T E, Seim E A (2018) Exploring software development at the very large-scale: A revelatory case study and research agenda for agile method adaptation. Empir Softw Eng 23:490–520 DOI: 10.1007/s10664-017-9524-2
• DiStaso M W, Bortree D S (2012) Multi-method analysis of transparency in social media practices: Survey, interviews and content analysis. Public Relat Rev 38(3):511–514 DOI: 10.1016/j.pubrev.2012.01.003
• Egelman C D, Murphy-Hill E, Kammer E, Hodges M M, Green C, Jaspan C, Lin J (2020) Predicting developers’ negative feelings about code review. In: International conference on software engineering. IEEE, pp 174–185
• Fan Y, Xia X, Alencar da Costa D, Lo D, Hassan A E, Li S (2019) The impact of changes mislabeled by SZZ on just-in-time defect prediction. Trans Softw Eng, 26
• Forsgren N, Storey M-A, Maddila C, Zimmermann T, Houck B, Butler J (2021) The SPACE of developer productivity: There’s more to it than you think. Queue 19(1):20–48 DOI: 10.1145/3454122.3454124
• Foundjem A, Adams B (2021) Release synchronization in software ecosystems. Empir Softw Eng. 26(34)
• Foundjem A, Constantinou E, Mens T, Adams B (2021a) Replication package — V2.0.0). 10.5281/zenodo.575996810.5281/zenodo.5759968, Online
• Foundjem A, Eghan E E, Adams B (2021b) Onboarding vs. diversity, productivity and quality – empirical study of the OpenStack ecosystem. In: International conference on software engineering, pp 1033–1045
• Fusch P, Ness L (2015) Are we there yet? data saturation in qualitative research, Qual Rep
• Fusch P I, Ness L R (2015) Are we there yet? data saturation in qualitative research. The qualitative report 20(9):1408
• Garson G D (2013) Path analysis. Statistical Associates Publishing Asheboro, NC
• German D M, Adams B, Hassan A E (2016) Continuously mining distributed version control systems: An empirical study of how Linux uses git. Empir Softw Eng 21(1):260–299 DOI: 10.1007/s10664-014-9356-2
• Ghaiumy Anaraky R, Li Y, Knijnenburg B (2021) Difficulties of measuring culture in privacy studies. Proc ACM Hum.-Comput Interact, 5(CSCW2). [Online]. Available: https://doi.org/10.1145/3479522
• Goeminne M, Mens T (2013) A comparison of identity merge algorithms for software repositories. Sci Comput Program 78:971–986 DOI: 10.1016/j.scico.2011.11.004
• Golzadeh M, Decan A, Constantinou E, Mens T (2021) Identifying bot activity in github pull request and issue comments. In: 2021 IEEE/ACM third international workshop on bots in software engineering (BotSE), pp 21–25
• Gopal A, Mukhopadhyay T, Krishnan M S (2005) The impact of institutional forces on software metrics programs. Trans Softw Eng 31(8):679–694 DOI: 10.1109/TSE.2005.95
• Guest G, Bunce A, Johnson L (2006) How many interviews are enough? an experiment with data saturation and variability. Field Methods 18 (1):59–82 DOI: 10.1177/1525822X05279903
• Henley A Z, Muçlu K, Christakis M, Fleming S D, Bird C (2018) CFar: A tool to increase communication, productivity, and review quality in collaborative code reviews. In: CHI. ACM, pp 1–13
• Hess M, Kromrey J (2004) Robust confidence intervals for effect sizes: A comparative study of Cohen’s d and Cliff’s delta under non-normality and heterogeneous variances. AERA, 1–30
• Himmelsbach J, Schwarz S, Gerdenitsch C, Wais-Zechmann B, Bobeth J, Tscheligi M (2019) Do we care about diversity in human computer interaction. In: International conference on human factors in computing systems. ACM, pp 1–16
• Igolkina A A, Meshcheryakov G (2020) semopy: A python package for structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal 27(6):952–963. [Online]. Available: 10.1080/10705511.2019.1704289 DOI: 10.1080/10705511.2019.1704289
• Islam M S, Khreich W, Hamou-Lhadj A (2018) Anomaly detection techniques based on kappa-pruned ensembles. IEEE Trans Reliab 67(1):212–229 DOI: 10.1109/TR.2017.2787138
• Izquierdo-Cortazar D, Sekitoleko N, Gonzalez-Barahona JM, Kurth L (2017) Using metrics to track code review performance. In: Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, EASE’17. [Online]. Available: 10.1145/3084226.3084247. Association for Computing Machinery, New York, pp 214–223
• Johnson D R, Creech J C (1983) Ordinal measures in multiple indicator models: A simulation study of categorization error. Am Sociol Rev, 398–407
• Kalliamvakou E, Damian D, Blincoe K, Singer L, German D M (2015) Open source-style collaborative development practices in commercial projects using GitHub. In: International conference on software engineering. IEEE, pp 574–585
• Kang H, Ahn J-W (2021) Model setting and interpretation of results in research using structural equation modeling: A checklist with guiding questions for reporting. Asian Nursing Research 15(3):157–162 DOI: 10.1016/j.anr.2021.06.001
• Kim M, Zimmermann T, DeLine R, Begel A (2016) The emerging role of data scientists on software development teams. In: 2016 IEEE/ACM 38th international conference on software engineering (ICSE). IEEE, pp 96–107
• Klem L (2000) Structural equation modeling
• Kononenko O, Baysal O, Godfrey M W (2016) Code review quality: How developers see it. In: Proceedings of the 38th international conference on software engineering, ICSE ’16. [Online]. Available: https://doi.org/10.1145/2884781.2884840. Association for Computing Machinery, New York, pp 1028–1038
• Kovalenko V, Bacchelli A (2018) Code review for newcomers: Is it different?. In: Proceedings of the 11th international workshop on cooperative and human aspects of software engineering, CHASE ’18. [Online]. Available: https://doi.org/10.1145/3195836.3195842. Association for Computing Machinery, New York, pp 29–32
• Krusche S, Berisha M, Bruegge B (2016) Teaching code review management using branch based workflows. In: International conference on software engineering. ACM, pp 384–393
• Landis J R, Koch G G (1977) The measurement of observer agreement for categorical data. Biometrics, 33(1)
• Lenberg P, Tengberg L G W, Feldt R (2017) An initial analysis of software engineers’ attitudes towards organizational change. Empir Softw Eng 22 (4):2179–2205 DOI: 10.1007/s10664-016-9482-0
• Lui KM, Chan KCC (2006) Pair programming productivity: Novice–novice vs. expert–expert. International Journal of Human-Computer Studies 64 (9):915–925. 10.1016/j.ijhcs.2006.04.010 DOI: 10.1016/j.ijhcs.2006.04.010
• Mardi F, Miller K, Balcerzak P (2021) Novice - expert pair coaching: Teaching Python in a pandemic. In: Technical symposium on computer science education. ACM, pp 226–231
• McHugh M L (2013) The chi-square test of independence. Biochemia Medica: Biochemia Medica 23(2):143–149 DOI: 10.11613/BM.2013.018
• McIntosh S, Kamei Y, Adams B, Hassan A E (2014) The impact of code review coverage and code review participation on software quality. In: Working conference on mining software repositories. ACM, pp 192–201
• McIntosh S, Kamei Y, Adams B, Hassan A E (2014) The impact of code review coverage and code review participation on software quality: A case study of the qt, vtk, and itk projects. In: Proceedings of the 11th working conference on mining software repositories, MSR 2014. [Online]. Available: https://doi.org/10.1145/2597073.2597076. Association for Computing Machinery, New York, pp 192–201
• McIntosh S, Kamei Y, Adams B, Hassan A E (2016) An empirical study of the impact of modern code review practices on software quality. Empir Softw Eng 21(5):2146–2189 DOI: 10.1007/s10664-015-9381-9
• Mens T, Cataldo M, Damian D (2019) The social developer: The future of software development. IEEE Softw 36(1):4 DOI: 10.1109/MS.2018.2874316
• Meshcheryakov G, Igolkina A A, Samsonova M G (2021) semopy 2: A structural equation modeling package with random effects in python. arXiv: 2106.01140
• Meyer A N, Barr E T, Bird C, Zimmermann T (2019) Today was a good day: The daily life of software developers. IEEE Trans Softw Eng 47 (5):863–880 DOI: 10.1109/TSE.2019.2904957
• Mlouki O, Khomh F, Antoniol G (2016) On the detection of licenses violations in the android ecosystem. In: International conference on software analysis, evolution, and reengineering. IEEE, pp 382–392
• Mukadam M, Bird C, Rigby PC (2013) Gerrit software code review data from Android. In: International working conference on mining software repositories, pp 45–48
• Neto E C, d Costa D A, Kulesza U (2019) Revisiting and improving SZZ implementations. In: International symposium on empirical software engineering and measurement, pp 1–12
• Neumayr T, Jetter H-C, Augstein M, Friedl J, Luger T (2018) Domino: A descriptive framework for hybrid collaboration and coupling styles in partially distributed teams. Human-Computer Interaction, 24
• Oliveira E, Fernandes E, Steinmacher I, Cristo M, Conte T, Garcia A (2020) Code and commit metrics of developer productivity: a study on team leaders perceptions. Empir Softw Eng 25(4):2519–2549 DOI: 10.1007/s10664-020-09820-z
• Plonka L, Sharp H, van der Linden J, Dittrich Y (2015) Knowledge transfer in pair programming: An in-depth analysis. International Journal of Human-Computer Studies 73:66–78 DOI: 10.1016/j.ijhcs.2014.09.001
• Rahman M T (2015) Investigating modern release engineering practices. In: International conference on software analysis, evolution, and reengineering. IEEE, pp 607–608
• Rich J T, Neely J G, Paniello R C, Voelker C C J, Nussenbaum B, Wang E W (2010) A practical guide to understanding kaplan-meier curves. Otolaryngology–head and neck surgery 143(3):331–6 DOI: 10.1016/j.otohns.2010.05.007
• Rigby P, Cleary B, Painchaud F, Storey M-A, German D (2012) Contemporary peer review in action: Lessons from open source development. IEEE Softw 29(6):56–61 DOI: 10.1109/MS.2012.24
• Rigby P C, German D M, Storey M-A (2008) Open source software peer review practices: A case study of the Apache server. In: International conference on software engineering. ACM, pp 541–550
• Rodriguez G, Robles G, Gonzalez-Barahona J (2018) Reproducibility and credibility in empirical software engineering. Inf Softw Technol 99:164–176 DOI: 10.1016/j.infsof.2018.03.009
• Runeson P, Host M, Rainer A, Regnell B (2012) Case study research in software engineering: Guidelines and examples, 1st edn. Wiley, Hoboken DOI: 10.1002/9781118181034
• Saldaña J (2015) The coding manual for qualitative researchers. SAGE Publications, Thousand Oaks
• Salleh N, Hoda R, Su M T, Kanij T, Grundy J (2018) Recruitment, engagement and feedback in empirical software engineering studies in industrial contexts. Inf Softw Technol 98:161–172 DOI: 10.1016/j.infsof.2017.12.001
• Satorra A, Bentler P M (2001) A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 66(4):507–514 DOI: 10.1007/BF02296192
• Sharp H, Robinson H (2008) Collaboration and co-ordination in mature extreme programming teams. International Journal of Human-Computer Studies 66(7):506–518 DOI: 10.1016/j.ijhcs.2007.10.004
• Siegmund J, Kästner C, Liebig J, Apel S, Hanenberg S (2014) Measuring and modeling programming experience. Empir Softw Eng 19(5):1299–1334 DOI: 10.1007/s10664-013-9286-4
• Śliwerski J, Zimmermann T, Zeller A (May 2005) When do changes induce fixes?. SIGSOFT Softw Eng Notes 30(4):1–5
• Spadini D, Aniche M, Bacchelli A (2018) PyDriller: Python framework for mining software repositories. In: Joint Meeting on ESEC and FSE. ACM, p 3
• Spohrer A H K, Kude T, Schmidt C T (2013) Peer-based quality assurance in information systems and development: A transactive memory perspective. In: International conference on information systems
• Steinmacher I, Silva M A G, Gerosa M A (2014) Barriers faced by newcomers to open source projects. In: Open Source Software: Mobile Open Source Technologies. Springer
• Terzimehić N, Háuslschmid R, Hussmann H, schraefel (2019) A review & analysis of mindfulness research in HCI. In: ICHF in computing systems. ACM, pp 1–13
• Tong C, Wong S K-S, Lui K P-H (2012) The influences of service personalization, customer satisfaction and switching costs on e-loyalty. International Journal of Economics and Finance 4(3):105–114 DOI: 10.5539/ijef.v4n3p105
• Treiblmaier H, Filzmoser P (2010) Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in is research. Information & Management 47(4):197–207 DOI: 10.1016/j.im.2010.02.002
• Tufano M, Palomba F, Bavota G, Oliveto R, Penta M D, De Lucia A, Poshyvanyk D (2017) When and why your code starts to smell bad (and whether the smells go away). Trans Softw Eng 43(11):1063–1088 DOI: 10.1109/TSE.2017.2653105
• Vallat R (2018) Pingouin: statistics in python. Journal of Open Source Software 3(31):1026. [Online]. Available: 10.21105/joss.01026 DOI: 10.21105/joss.01026
• Wen M, Wu R, Liu Y, Tian Y, Xie X, Cheung S-C, Su Z (2019) Exploring and exploiting the correlations between bug-inducing and bug-fixing commits. In: Joint Meeting on ESEC and FSE. ACM, pp 326–337
• Whitehead J (2007) Collaboration in software engineering: A roadmap. In: Future of software engineering, pp 214–225
• Wohlin C, Runeson P, Höst M, Ohlsson M C, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer Science & Business Media, Berlin DOI: 10.1007/978-3-642-29044-2
• Xia X, Lo D, Wang X, Yang X (2015) Who should review this change?: Putting text and file location analyses together for more accurate recommendations. In: 2015 IEEE international conference on software maintenance and evolution (ICSME), pp 261–270
• Young J-G, Casari A, McLaughlin K, Trujillo M Z, Hébert-Dufresne L, Bagrow J P (2021) Which contributions count? analysis of attribution in open source. In: International working conference on mining software repositories. IEEE
• Zhang Y, Zhou M, Mockus A, Jin Z (2021) Companies’ participation in oss development–an empirical study of openstack. IEEE Trans Softw Eng 47(10):2242–2259 DOI: 10.1109/TSE.2019.2946156
• Zhang Y, Zhou M, Stol K-J, Wu J, Jin Z (2020) How do companies collaborate in open source ecosystems?. In: International conference on software engineering. ACM, pp 1196–1208
• Zhou S, Vasilescu B, Kästner C (2020) How has forking changed in the last 20 years? A study of hard forks on GitHub. In: International conference on software engineering. ACM, pp 445–456