Open Source Is Not One Thing: A Typology of Open-Source Software Sub-Genres


Abstract

Open source software (OSS) is not homogeneous. A project’s purpose, governance, and funding shape how its community forms, who contributes, and how the software is maintained, yet empirical research often samples OSS broadly and reports findings as if they held for open source as a whole. We argue that OSS comprises distinguishable sub-genres, and that the sub-genre a study samples bounds how far its findings generalize. Using a light, multi-source review that screens 3,925 unique papers, we synthesize a typology of fourteen OSS sub-genres, from well-studied ones such as community-driven, company-backed, foundation-governed, research and scientific, and open source for social good (OSS4SG), to under-studied ones such as multi-company co-opetition, protestware, and open-source appropriate technology. We place the sub-genres in a framework that records each one’s primary driver, governance, and funding, with its maturity in the literature and representative projects, and we present a research agenda whose central question is whether findings established on one sub-genre transfer to others. The contribution is the typology and the agenda rather than a complete census, and we mark the sub-genres whose empirical support is thin.

1 Introduction↩︎

Open source software (OSS) is not one thing. Empirical research nonetheless samples it as though it were. Studies draw a set of popular GitHub projects, fit models of contributor retention, onboarding, or code quality, and report the results for open source in general. The implicit assumption is that a community Linux distribution, a single-vendor database, a university research library, a humanitarian health-records system, and a one-person utility differ only in size and popularity. They do not. They differ in why the project exists, who decides its direction, how the work is paid for, and how the community behaves.

Recent evidence makes the difference concrete. A comparison of mission-driven open source for social good (OSS4SG) and conventional OSS reports different community structure, contributor retention, and code-quality management, with OSS4SG communities retaining contributors (“sticky”) and conventional projects attracting many who do not stay (“magnetic”) [1]. The newcomer-to-core pathway also differs by sub-genre, and contributors in OSS4SG reach core status at higher rates and through more than one route [2]. Even the way a newcomer first joins, whether through a mentorship program, a hackathon, or independently, is associated with how long they stay [3]. When two sub-genres diverge this far, a retention model or an onboarding program validated on one of them need not hold for the others. Coarse metrics such as stars and commit counts hide the structure that decides whether a finding generalizes.

The distinction matters in practice as well. A developer deciding where to contribute gains from knowing what kind of project they are approaching, because its governance, funding, and community behavior set what a first contribution takes and how a newcomer is received. Social barriers at the first contribution already push newcomers to abandon projects [4], and the path into a single-vendor product differs from the path into a volunteer distribution or a research library. Sub-genre bears on sustainability too. How a project is funded and who maintains it, and so how resilient it is, ranges from firm-employed teams to the few unpaid volunteers behind widely used infrastructure [5], [6]. Naming a project’s sub-genre makes these differences explicit instead of leaving each contributor and each study to rediscover them.

This paper makes three contributions. First, we present a typology of fourteen OSS sub-genres (Section 4), organized by who drives a project and to what end. Second, we place the sub-genres in a framework that records their driver, governance, and funding (Section 5). Third, we present a research agenda that treats sub-genre as a variable to report and control for in empirical OSS studies (Section 6). The paper is short by design. We mark the sub-genres whose evidence is thin as targets for future work.

2 Related Work↩︎

Prior work classifies open source along single axes. [7] contrasts a volunteer “OSS 1.0” with a commercially co-opted “OSS 2.0”. [8] separates community open source from single-vendor commercial open source, and [9] contrasts community-managed projects with firm-involved hybrids. Other schemes classify by the firm-community relationship [10], by governance configuration [11], [12], by business model [13], by contributor motivation [14], or by structural growth dynamics, as in the stadiums, clubs, federations, and toys of [15]. A broad survey of free and open-source development covers the literature across topics from individual contributions to governance and community dynamics [16].

These schemes are valuable but partial. Each cuts the space on one dimension, and most resolve into a binary or a few types that center on commercial form and leave little room for non-commercial and public-interest projects. The typology presented here differs in two ways. It organizes OSS by who drives a project and to what end, an axis that the commercial-versus-community binaries approximate but do not fully separate, and it spans fourteen sub-genres that include forms the earlier schemes collapse or omit, such as multi-company co-opetition, government and civic OSS, open-source appropriate technology, protestware, and critical digital infrastructure.

3 Method↩︎

We conducted a light, multi-source review rather than an exhaustive systematic one, and we release the search-and-screen script so the corpus can be regenerated. We queried two scholarly indexes, OpenAlex and arXiv, with 46 queries grouped into fifteen facets, one facet per candidate sub-genre plus a discovery facet aimed at sub-genres we had not anticipated, such as “taxonomy of open source projects”. The search returned 5,771 records. We normalized them to a common schema and removed duplicates by DOI or title, which left 3,925 unique papers. We then screened the papers with a rule that keeps a paper when its title or abstract both refers to open source and signals that the work defines, classifies, or studies a kind of OSS project or community. Of these, 399 papers passed. Coverage across the fourteen sub-genres ranged from 8 papers, for critical digital infrastructure, to 76, for OSS4SG, and reflects query reach rather than the size of each field. We grouped the screened papers into the sub-genres below. The search-and-screen design follows the bibliometric and systematic-review practice used in prior work [17], [18].

We treat the resulting typology as a working hypothesis to be confirmed, split, merged, and extended, not as a settled ontology, and Section 7 states its limits.

4 A Typology of OSS Sub-Genres↩︎

Table 1: A typology of OSS sub-genres, recording each one’s primary driver, governance, funding, maturity in the literature, and representative projects. Maturity reflects the depth ofdedicated empirical literature, where established marks a substantial body of work, growingmarks an active but smaller one, and emerging marks only a handful of dedicated studies. Thesub-genres are not mutually exclusive, and many real projects span more than one.
Sub-genre Primary driver Governance Funding Maturity Examples
Community-driven Intrinsic: learning, reputation, ideology Meritocratic, peer-elected Volunteer, donations Established Debian, Arch Linux, GNU Emacs
Company-backed OSS Commercial: revenue, market control One firm controls Firm-employed staff, product revenue Established Elastic, MongoDB, GitLab
Foundation-governed Neutral multi-party collaboration Foundation rules, vendor-neutral Member dues, corporate sponsorship Established Apache HTTP, Kubernetes, Eclipse
Multi-company co-opetition Strategic shared work among rivals Shared across firms Competing firms’ staff Emerging OpenStack, Linux kernel
InnerSource Internal reuse, efficiency Trusted committers within a firm Corporate, internal Growing HP, Philips, Ericsson
OSS for Social Good (OSS4SG) Societal benefit NGO, nonprofit, community Grants, donations, NGOs Growing OpenMRS, DHIS2, Ushahidi
Government and civic Public mandate, transparency Publicly accountable Public funds, procurement Growing Decidim, X-Road, GovStack
Educational Learning and social good Faculty or instructor led Universities, course staff Growing Sahana Eden, LibreFoodPantry
Open-source appropriate technology Basic needs, sustainability Practitioner and academic networks Grants, NGOs Emerging RepRap, WikiHouse
Hobbyist and solo Personal enjoyment, learning None, single maintainer Unpaid, occasional donations Established left-pad, micrograd
Protestware Political or economic protest Maintainer acts unilaterally Unpaid Emerging node-ipc, colors.js
Research and scientific (RSE) Research support, reproducibility Laboratory or PI led Research grants Established Astropy, scikit-learn, LAMMPS
Open hardware and open data Reproducibility, maker, civic data Domain consortia, communities Grants, community Growing Arduino, OpenStreetMap
Critical digital infrastructure Public good, technical interest Informal, few maintainers Under-funded, ad-hoc sponsorship Growing OpenSSL, curl, log4j

We organize the sub-genres by their primary driver, that is, who starts and sustains the project and to what end. The axis is not perfectly clean, since many projects span more than one sub-genre, but it captures the main source of variation. Table 1 summarizes all fourteen sub-genres by primary driver, governance, and funding, with maturity and examples. The subsections below define each sub-genre and cite representative work, and we mark the ones whose dedicated literature is still thin as emerging.

4.1 Firm-driven open source↩︎

Company-backed OSS is owned and primarily developed by one firm that earns revenue from the software, through an open-core model (a free core paired with paid proprietary extensions), dual licensing, or a hosted service, while the firm controls the project’s direction. [8] defines the single-vendor commercial form, and later studies analyze how firms participate in and govern such projects [19], [20]. Multi-company co-opetition is an emerging sub-genre in which firms that compete in the market jointly develop shared infrastructure. [21] examined cooperation among competing firms in OpenStack [22], and recent work characterizes how such collaborations are governed [23]. InnerSource applies open-source practices inside a single organization, adding open contribution and trusted-committer review to internal development for cross-team reuse [24][26].

4.2 Community- and foundation-governed open source↩︎

Community-driven OSS is the classic peer-production model, built by volunteers who contribute from intrinsic motivation and where authority follows merit rather than ownership. [27] established the economic theory of commons-based peer production, and empirical studies describe the social and authority structure of projects such as Debian [28], [29]. Foundation-governed OSS places a project under a vendor-neutral nonprofit, such as the Apache Software Foundation, the Linux Foundation, or the Eclipse Foundation, that holds the trademark and intellectual property and enforces neutral rules so that competing firms can collaborate [30][32].

4.3 Mission- and impact-driven open source↩︎

Open source for social good (OSS4SG) comprises projects whose primary purpose is societal benefit rather than commercial value, such as health-records systems and crisis-response tools, and many appear in digital public goods registries. [33] studies the motivations and challenges of contributing to OSS4SG, and later work examines its contributors and community dynamics [1], [2], [34]. Government and civic OSS, including software designated as a digital public good, is commissioned or mandated by public institutions under transparency and procurement constraints [35][37]. Educational OSS is produced inside teaching contexts, with students contributing for course credit. The Humanitarian Free and Open Source Software (HFOSS) tradition is its archetype, pairing software-engineering education with socially beneficial projects [38][40]. Open-source appropriate technology (OSAT) is an emerging sub-genre that extends open-source practice from software to hardware and design knowledge meant to meet basic needs in resource-limited settings. [41] articulates OSAT and demonstrates it in practice [42], [43].

4.4 Individual- and conflict-driven open source↩︎

Hobbyist and solo OSS consists of projects that one person builds for enjoyment or to meet a personal need, with no formal governance and a low truck factor, meaning that few contributors would have to leave for the project to stall [6], [44], [45]. Protestware is an emerging sub-genre in which a maintainer alters or sabotages a package to deliver a political or economic message, so that the defining act is conflict rather than feature work [46][48].

4.5 Knowledge- and infrastructure-driven open source↩︎

Research and scientific software (RSE) is built inside research to support and reproduce studies, usually by domain scientists rather than trained software engineers, and it is funded by grants. It is at once a research output and a research instrument [49][51]. Open-source hardware and open data apply open-source practice to non-software artifacts, including physical designs and public datasets [52][54]. Some projects release an open dataset as the primary artifact, such as in computer-vision-based fitness analysis [55]. Critical digital infrastructure OSS comprises components that much of the software ecosystem depends on indirectly, such as OpenSSL and curl, yet that a few volunteers often maintain. [5] named this sustainability problem, and later work formalizes it as underproduction, where the labor a component receives falls short of how widely it is relied upon [56]. Studies of the npm ecosystem also show how a few maintainers’ accounts reach much of it [57].

5 A Typology Framework↩︎

Table 1 shows two patterns. First, the primary-driver axis does not fix the other dimensions. Foundation-governed OSS and multi-company co-opetition both involve competing firms, yet they differ in where authority sits. Second, the maturity column shows where evidence is thin. Several sub-genres that matter in practice, namely multi-company co-opetition, protestware, and open-source appropriate technology, rest on a few studies each, against the deep literatures on community-driven, company-backed, and foundation-governed OSS and on research and scientific software.

6 Why It Matters: A Research Agenda↩︎

6.0.0.1 Transfer of findings across sub-genres.

This is the question the typology raises. A retention model built on company-backed OSS, an onboarding program validated on a large community project, or a code-quality heuristic drawn from popular repositories need not transfer to research software, humanitarian projects, or solo-maintained infrastructure. Prior work already shows that contributor retention, the newcomer-to-core pathway, and the effect of entry events differ between OSS4SG and conventional OSS [1][3]. How far this difference extends across the other sub-genres is open. Research and scientific software is a concrete next case, since its contributors work to research calendars and academic careers rather than product cycles, and whether community-structure findings from conventional OSS hold there is untested.

6.0.0.2 Under-studied sub-genres.

Critical digital infrastructure is the clearest case, a sub-genre the whole ecosystem depends on yet one defined by few maintainers and limited funding [5], [56]. Multi-company co-opetition and protestware are similarly consequential and similarly thin. A sub-genre-aware research program would direct effort toward these cases.

6.0.0.3 Reporting and controlling for sub-genre.

A study should report the sub-genre it samples, and control for it where possible, as studies already report programming language or project size. Tooling can help. Methods that recover intent from artifacts, such as generating user stories from source code with large language models [58], point toward classifying projects into sub-genres at scale, which would make sub-genre-stratified analysis routine. Classification at scale would also serve practice, letting a contributor see what kind of community they are joining before investing effort, and letting maintainers and funders find the sub-genres where support is thin.

7 Threats to Validity↩︎

The review is light rather than systematic. It covers English-language and well-indexed venues, so we may have missed sub-genres visible mainly in other languages or in grey literature. The sub-genres are not mutually exclusive. Kubernetes is at once foundation-governed and multi-company co-opetitive, and OpenMRS is at once OSS4SG and educational, so the sub-genres overlap rather than partition the space. The maturity labels reflect the literature we retrieved rather than a citation count, and they can understate a sub-genre whose evidence is recent or scattered. The per-sub-genre screened counts reflect the reach of the queries, not the true size of each field. The dimensions are deliberately coarse, and we leave finer stratification, such as by application domain, to future work.

8 Conclusion↩︎

Open source is plural. Treating it as one thing lets coarse metrics hide differences in purpose, governance, funding, and community behavior, differences large enough that a finding from one sub-genre need not hold for another. We have presented a typology of fourteen OSS sub-genres, placed them in a comparison framework, and proposed a research agenda whose first task is to test how far existing findings generalize. The typology is provisional. Its value is in making the variety of open source explicit, and in marking the emerging sub-genres, namely co-opetition, protestware, and appropriate technology, where new empirical work is most needed.

References↩︎

[1]
Mohamed Ouf, Shayan Noei, Zeph Van Iterson, Mariam Guizani, and Ying Zou.2026. . In Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering(ICSE ’26). ACM, New York, NY, USA, 13 pages.  [cs.SE]https://doi.org/10.1145/3744916.3787782.
[2]
Mohamed Ouf, Amr Mohamed, and Mariam Guizani.2026. . In Proceedings of the 30th International Conference on Evaluation and Assessment in Software Engineering(EASE ’26). ACM, New York, NY, USA.  [cs.SE].
[3]
Mohamed Ouf Mariam Guizani.2026. . In Proceedings of the 30th International Conference on Evaluation and Assessment in Software Engineering(EASE ’26). ACM, New York, NY, USA.  [cs.HC].
[4]
Igor Steinmacher, Tayana Conte, Marco Aurélio Gerosa, and David Redmiles.2015. . In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW 2015). ACM, New York, NY, USA, 1379–1392. https://doi.org/10.1145/2675133.2675215.
[5]
Nadia Eghbal.2016. Roads and Bridges: The Unseen Labor Behind Our Digital Infrastructure. Technical Report. Ford Foundation. ://www.fordfoundation.org/work/learning/research-reports/roads-and-bridges-the-unseen-labor-behind-our-digital-infrastructure/.
[6]
Jailton Coelho Marco Tulio Valente.2017. . In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2017). ACM, New York, NY, USA, 186–196. https://doi.org/10.1145/3106237.3106246.
[7]
Brian Fitzgerald.2006. . MIS Quarterly30, 3(2006), 587–598. https://doi.org/10.2307/25148740.
[8]
Dirk Riehle.2012. . Information Systems and e-Business Management10, 1(2012), 5–17. https://doi.org/10.1007/s10257-010-0149-x.
[9]
Siobhán O’Mahony.2007. Journal of Management and Governance11, 2(2007), 139–150. https://doi.org/10.1007/s10997-007-9024-7.
[10]
Linus Dahlander Mats G. Magnusson.2005. . Research Policy34, 4(2005), 481–493. https://doi.org/10.1016/j.respol.2005.02.003.
[11]
Dany Di Tullio D. Sandy Staples.2013. . Journal of Management Information Systems30, 3(2013), 49–80. https://doi.org/10.2753/MIS0742-1222300303.
[12]
M. Lynne Markus.2007. Journal of Management & Governance11, 2(2007), 151–163. https://doi.org/10.1007/s10997-007-9021-x.
[13]
Estelle Duparc, Frederik Möller, Ilka Jussen, Maleen Stachon, Sükran Algac, and Boris Otto.2022. . Electronic Markets32, 2(2022), 727–745. https://doi.org/10.1007/s12525-022-00557-9.
[14]
Sonali K. Shah.2006. . Management Science52, 7(2006), 1000–1014. https://doi.org/10.1287/mnsc.1060.0553.
[15]
Nadia Eghbal.2020. Working in Public: The Making and Maintenance of Open Source Software. Stripe Press, San Francisco, CA, USA.
[16]
Kevin Crowston, Kangning Wei, James Howison, and Andrea Wiggins.2012. . Comput. Surveys44, 2(2012), 7:1–7:35. https://doi.org/10.1145/2089125.2089127.
[17]
Abdelmoneim Soliman, Mervin A. Marshall, Md Safiqur Rahaman, Mohamed Ashraf Ouf, and Ahmed El-Sayed.2024. . The Journal of Ocean Technology19, 2(2024), 46–76.
[18]
Mohamed Ashraf Ouf, Abdelrahman Elkhateeb, Abdelmoneim Soliman, Md Safiqur Rahaman, Sara Mobarak, and Mervin A. Marshall.2024. . The Journal of Ocean Technology19, 4(2024), 52–84.
[19]
Joel West Siobhán O’Mahony.2008. . Industry and Innovation15, 2(2008), 145–168. https://doi.org/10.1080/13662710801970142.
[20]
Lars Dahlander Mats Magnusson.2008. Long Range Planning41, 6(2008), 629–649. https://doi.org/10.1016/j.lrp.2008.09.003.
[21]
José Teixeira, Salman Mian, and Ulla Hytti.2016. . In Proceedings of the 37th International Conference on Information Systems (ICIS 2016). Association for Information Systems, Dublin, Ireland.
[22]
José Teixeira, Gregorio Robles, and Jesús M. González-Barahona.2015. . Journal of Internet Services and Applications6, 1(2015), 14. https://doi.org/10.1186/s13174-015-0028-2.
[23]
Cailean Osborne, Farbod Daneshyan, Runzhi He, Hengzhi Ye, Yuxia Zhang, and Minghui Zhou.2025. . Proceedings of the ACM on Human-Computer Interaction9, CSCW2(2025), CSCW046:1–CSCW046:30. https://doi.org/10.1145/3710944.
[24]
Maximilian Capraro Dirk Riehle.2016. . Comput. Surveys49, 4, Article 67(Dec.2016), 36 pages. https://doi.org/10.1145/2856821.
[25]
Klaas-Jan Stol, Paris Avgeriou, Muhammad Ali Babar, Yan Lucas, and Brian Fitzgerald.2014. . ACM Transactions on Software Engineering and Methodology23, 2, Article 18(April2014), 35 pages. https://doi.org/10.1145/2533685.
[26]
Klaas-Jan Stol Brian Fitzgerald.2015. . IEEE Software32, 4(2015), 60–67. https://doi.org/10.1109/MS.2014.77.
[27]
Yochai Benkler.2002. . Yale Law Journal112, 3(2002), 369–446. https://doi.org/10.2307/1562247.
[28]
Kevin Crowston James Howison.2005. . First Monday10, 2(2005). https://doi.org/10.5210/fm.v10i2.1207.
[29]
Siobhán O’Mahony Fabrizio Ferraro.2007. . Academy of Management Journal50, 5(2007), 1079–1106. https://doi.org/10.5465/amj.2007.27169153.
[30]
Javier Luis Cánovas Izquierdo Jordi Cabot.2018. . In Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Society(ICSE-SEIS ’18). ACM, New York, NY, USA, 3–12. https://doi.org/10.1145/3183428.3183438.
[31]
Juan C. Dueñas, Hugo A. Parada, Félix Cuadrado, Manuel Santillán, and José L. Ruiz.2007. . IEEE Software24, 6(2007), 90–98. https://doi.org/10.1109/MS.2007.157.
[32]
Nan Yang, Igor Ferreira, Alexander Serebrenik, and Bram Adams.2022. . In Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Society(ICSE-SEIS ’22). IEEE, Piscataway, NJ, USA, 161–171. https://doi.org/10.1109/ICSE-SEIS55304.2022.9794012.
[33]
Yu Huang, Denae Ford, and Thomas Zimmermann.2021. . In Proceedings of the 43rd International Conference on Software Engineering (ICSE). IEEE, Piscataway, NJ, USA, 1020–1032. https://doi.org/10.1109/ICSE43902.2021.00096.
[34]
Zihan Fang, Madeline Endres, Thomas Zimmermann, Denae Ford, Westley Weimer, Kevin Leach, and Yu Huang.2023. . In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). ACM, New York, NY, USA, 3–15. https://doi.org/10.1145/3611643.3616250.
[35]
Johan Linåker, Gregorio Robles, Deborah Bryant, and Sachiko Muto.2023. . IEEE Software40, 4(2023), 39–44. https://doi.org/10.1109/MS.2023.3266105.
[36]
Johan Linåker, Björn Lundell, Francisco Servant, Jonas Gamalielsson, Sachiko Muto, and Gregorio Robles.2025. Empirical Software Engineering30, 3(2025). https://doi.org/10.1007/s10664-025-10626-0.
[37]
Annelies van Loon Dimiter Toshkov.2015. . Government Information Quarterly32, 2(2015), 207–215. https://doi.org/10.1016/j.giq.2015.01.004.
[38]
Heidi J. C. Ellis, Ralph Morelli, Trishan de Lanerolle, Joanna Damon, and Joel Raye.2007. . In Proceedings of the 38th ACM Technical Symposium on Computer Science Education (SIGCSE 2007). ACM, New York, NY, USA, 551–555. https://doi.org/10.1145/1227310.1227495.
[39]
Gregory W. Hislop, Heidi J. C. Ellis, S. Monisha Pulimood, Becka Morgan, Suzanne Mello-Stark, Ben Coleman, and Cam Macdonell.2015. . In Proceedings of the 11th Annual International ACM Conference on International Computing Education Research (ICER 2015). ACM, New York, NY, USA, 199–206. https://doi.org/10.1145/2787622.2787726.
[40]
Gustavo Pinto, Clarice Ferreira, Cleice Souza, Igor Steinmacher, and Paulo Meirelles.2019. . In Proceedings of the 41st IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET 2019). IEEE, Piscataway, NJ, USA, 147–157. https://doi.org/10.1109/ICSE-SEET.2019.00024.
[41]
Joshua M. Pearce.2012. . Environment, Development and Sustainability14, 3(2012), 425–431. https://doi.org/10.1007/s10668-012-9337-9.
[42]
Joshua M. Pearce, Christine Morris Blair, Kristen J. Laciak, Rob Andrews, Amir Nosrat, and Ivana Zelenika-Zovko.2010. . Journal of Sustainable Development3, 4(2010), 17–29. https://doi.org/10.5539/jsd.v3n4p17.
[43]
Joshua M. Pearce.2012. . Science337, 6100(2012), 1303–1304. https://doi.org/10.1126/science.1228183.
[44]
Eirini Kalliamvakou, Georgios Gousios, Kelly Blincoe, Leif Singer, Daniel M. German, and Daniela Damian.2014. . In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR 2014). ACM, New York, NY, USA, 92–101. https://doi.org/10.1145/2597073.2597074.
[45]
Guilherme Avelino, Leonardo Passos, Andre Hora, and Marco Tulio Valente.2016. . In Proceedings of the 24th IEEE International Conference on Program Comprehension (ICPC 2016). IEEE, Piscataway, NJ, USA, 1–10. https://doi.org/10.1109/ICPC.2016.7503718.
[46]
Youmei Fan, Dong Wang, Supatsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, and Raula Gaikovina Kula.2025. . Empirical Software Engineering(2025). https://doi.org/10.1007/s10664-024-10599-6.
[47]
Youmei Fan, Dong Wang, Supatsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, and Raula Gaikovina Kula.2024. . In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering: Companion Proceedings(ICSE ’24 Companion). ACM, New York, NY, USA, 308–309. https://doi.org/10.1145/3639478.3643086.
[48]
Marc Cheong, Raula Gaikovina Kula, and Christoph Treude.2023. Ethical Considerations Towards Protestware. arXiv preprint arXiv:2306.10019.  [cs.CY]https://doi.org/10.48550/arXiv.2306.10019.
[49]
Greg Wilson, D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, Kathryn D. Huff, Ian M. Mitchell, Mark D. Plumbley, Ben Waugh, Ethan P. White, and Paul Wilson.2014. . PLOS Biology12, 1(2014), e1001745. https://doi.org/10.1371/journal.pbio.1001745.
[50]
Jo Erskine Hannay, Carolyn MacLeod, Janice Singer, Hans Petter Langtangen, Dietmar Pfahl, and Greg Wilson.2009. . In Proceedings of the ICSE Workshop on Software Engineering for Computational Science and Engineering (SECSE). IEEE, Piscataway, NJ, USA, 1–8. https://doi.org/10.1109/SECSE.2009.5069155.
[51]
Dustin Heaton Jeffrey C. Carver.2015. . Information and Software Technology67(2015), 207–219. https://doi.org/10.1016/j.infsof.2015.07.011.
[52]
Jérémy Bonvoisin, Tom Buchert, Maurice Preidel, and Rainer G. Stark.2018. . Design Science4(2018), e19. https://doi.org/10.1017/dsj.2018.15.
[53]
Jean-François Boujut, Franck Pourroy, Philippe Marin, Jason Dai, and Gilles Richardot.2019. . In Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1. Cambridge University Press, 2307–2316. https://doi.org/10.1017/dsi.2019.237.
[54]
Nama R. Budhathoki Caroline Haythornthwaite.2013. . American Behavioral Scientist57, 5(2013), 548–575. https://doi.org/10.1177/0002764212469364.
[55]
Mohamed Ashraf Ouf, Abdulrahman Hussien Ali, Rowan Mohamed Amin, Yehia Ahmed Hamdan, Moustafa Mamdouh Sabry, and Sherine Nagy Saleh.2024. . In 2024 International Telecommunications Conference (ITC-Egypt). IEEE, Piscataway, NJ, USA, 428–433. https://doi.org/10.1109/ITC-EGYPT61547.2024.10620528.
[56]
Kaylea Champion Benjamin Mako Hill.2021. . In Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021). IEEE, Piscataway, NJ, USA, 388–399. https://doi.org/10.1109/SANER50967.2021.00043.
[57]
Markus Zimmermann, Cristian-Alexandru Staicu, Cam Tenny, and Michael Pradel.2019. . In Proceedings of the 28th USENIX Security Symposium (USENIX Security ’19). USENIX Association, 995–1010.
[58]
Mohamed Ouf, Haoyu Li, Michael Zhang, and Mariam Guizani.2025. . In Proceedings of the 2025 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON 2025). IEEE, Piscataway, NJ, USA, 504–509.  [cs.SE]https://doi.org/10.1109/CASCON66301.2025.00080.