Pith. sign in

REVIEW 5 major objections 8 minor 130 references

Who you are and why you contribute shape which open-source projects you prefer—and newcomers and veterans diverge.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.5

2026-07-31 00:34 UTC pith:3FS5V5ZI

load-bearing objection Useful comparative survey of newcomers vs veterans on motives and project prefs, undercut by an overstated multiple-testing story. the 5 major comments →

arxiv 2607.25126 v1 pith:3FS5V5ZI submitted 2026-07-27 cs.SE

What Motivates Whom? A Survey of Newcomers to OSS and Experienced OSS Practitioners

Shashiwadana Nirmani , Hourieh Khalajzadeh , Mojtaba Shahin , Xiao Liu This is my paper
classification cs.SE
keywords MotivationsOpen Source SoftwareRecommendation SystemsDemographicsNewcomersContributor onboardingProject selectionSurvey
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Software practitioners often pick the wrong open-source projects, stall onboarding, and drop out. This survey of 208 people with software backgrounds shows that age, gender, region, education, experience, and OSS role correlate with why people contribute, and those motivations in turn correlate with preferences for project age, stage, documentation, community size, and related traits. The paper splits newcomers who have never contributed from experienced practitioners and reports distinct patterns for each group. Practitioners also say recommendation tools should be interactive, personalized to growth goals, and sensitive to collaboration style. If these links hold, maintainers and tool builders can target onboarding and retention instead of treating all contributors alike.

Core claim

Demographic factors significantly correlate with OSS contribution motivations, motivations significantly correlate with stated preferences for project characteristics such as age, development stage, and documentation quality, and these patterns differ when newcomers to OSS and experienced practitioners are analyzed separately; respondents also want recommendation systems that reflect motivations, growth, and collaboration preferences.

What carries the argument

A comparative online survey of 208 screened practitioners (85 newcomers, 123 experienced), analyzed with Kruskal-Wallis tests (Bonferroni-corrected) on Likert and categorical responses linking demographics to seven motivations and eight project characteristics, plus inductive thematic analysis of open-ended recommender feedback.

Load-bearing premise

One-time closed-ended self-reports of motivations and hypothetical project preferences from a convenience sample after heavy screening attrition truly reflect how people choose projects in the wild.

What would settle it

Track real join decisions and retention for newcomers and veterans whose stated motivations and demographics match the survey cells, and check whether they disproportionately enter projects with the preferred traits (new/active, clear guidelines, source comments, small communities, etc.) versus unmatched projects.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Maintainers can tag tasks and repos by motivation cues (learning, helping, career) and demographic-aligned traits so the right people find them.
  • Recommendation systems should let users filter or converse about skills, growth goals, and collaboration style rather than only popularity or language.
  • Newcomer onboarding and veteran retention need different project signals; one-size defaults will miss both groups.
  • Recognition badges and maintainer dashboards can surface motivation-aligned opportunities without treating paid and volunteer contributors identically.
  • Longitudinal and domain-specific follow-ups can test whether motivation–preference links shift as people move from casual to core roles.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If region and experience effects on incentives, reputation, and networking replicate, global projects may need locale-specific contribution pathways rather than English-centric defaults alone.
  • Cold-start recommenders that ask a short motivation questionnaire could outperform history-only models for true newcomers who have no starring or forking trail.
  • Merging sparse demographic cells for power may hide intersectional patterns (e.g., young women in Africa) that matter most for inclusion interventions.
  • Practitioner demand for LinkedIn-style cross-platform signals implies privacy and consent design will become as central as ranking quality.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 8 minor

Summary. The paper reports an online survey of 208 software practitioners (85 "newcomers to OSS" with SE backgrounds but no OSS contributions, 123 experienced OSS practitioners), recruited via Prolific and LinkedIn and screened with Danilova-style programming questions (632 recorded → 208 analyzed). It examines correlations between demographics and seven OSS motivations (RQ1), motivations and eight project-characteristic preferences (RQ2), and demographics and preferences (RQ3), using Kruskal–Wallis omnibus tests with Bonferroni post-hoc correction and ε² effect sizes, analyzed separately for the two groups. RQ4 thematically analyzes an open-ended item on improving recommendation systems, yielding four themes (personal fit/growth, project characteristics, interactive/evolving recommenders, collaboration/team dynamics). The design is standard and mostly careful: ethics approval, pilot, mandatory items, dual-coder thematic analysis with reported κ, and a public replication package. However, the statistical reporting is internally inconsistent: the blanket claim that all reported findings have Bonferroni-adjusted p<0.05 is contradicted by the paper's own tables (p=0.051, 0.054, 0.242), and several reported "significant" pairwise differences of 1–5% are not credible under any correction at n=123. These issues are load-bearing for the RQ1–RQ3 claims.

Significance. If the results hold, the paper offers a useful comparative descriptive map of motivations and project-selection preferences across newcomer and experienced contributor groups — a split few prior surveys make explicitly — with direct implications for OSS recommender design and maintainer practice. Strengths worth naming: a public replication package with the full instrument, a validated screening procedure, effect sizes reported alongside p-values, and dual-coded thematic analysis. The contribution is incremental over Gerosa et al. (2021) and Qiu et al. (2019) rather than transformative, and all evidence is single-wave, self-reported, stated-preference data from a convenience sample; the findings are correlational and exploratory in character. The paper's value depends heavily on the credibility of the reported significance structure, which currently is in doubt.

major comments (5)
  1. [§4.1–§4.3, Tables 4, 7, 10, 18] The paper states (§4.1, repeated §4.2/§4.3) that 'All findings presented in this paper were considered statistically significant, with Bonferroni adjusted p-values less than 0.05.' This is contradicted by the results tables: Table 7 includes Incentives×SE-Experience p=0.054; Table 10 includes Learning×Multilingual-Documentation p=0.051; Table 18 includes Region×Project-Age p=0.242 — all with effect sizes, inside tables of purportedly significant results. Either these rows should not be presented as findings, or the blanket claim is false. Relatedly, §4.3.2 reports a post-hoc Africa-vs-Europe difference on project age (p=0.049) while the corresponding omnibus test in Table 18 is p=0.242; pairwise post-hoc tests after a non-significant omnibus are not valid, yet this specific contrast is elevated into the Introduction as a headline result.
  2. [§3.4, §4.1, Tables 4–18] It is unclear what 'Bonferroni adjusted' means here. If correction is only within each omnibus test's pairwise family, the RQ1 family alone is ~7 motivations × 7 demographics × 2 groups ≈ 98 omnibus tests (plus ~112 in RQ2 and more in RQ3), so marginal cells at p≈0.03–0.05 (e.g., Table 4: Learning×Gender 0.035, Learning×SE-experience 0.049) are at the expected false-positive rate. The authors must define the correction family, state whether tabled p-values are raw omnibus or adjusted post-hoc values, and either apply a family-level policy (e.g., FDR) or reframe all near-threshold results as exploratory. Text/table values also disagree: §4.1.1 text gives p=0.038 for Learning×Gender where Table 4 gives 0.035.
  3. [§4.2.2, Tables 12–14] Several reported significant post-hoc contrasts are implausible at this sample size: §4.2.2 reports Enjoyment×Multilingual-Documentation '1% more … (p=0.027)', Networking×Web-page '3% more … (p=0.042)', Incentives×Multilingual '8% more … (p=0.024)'. With n=123 split across five intensity levels, a 1–5% difference corresponds to roughly one respondent; such a contrast cannot yield a Bonferroni-adjusted p<0.05. This strongly suggests the reported p-values are uncorrected or mis-computed. These cells should be audited and re-reported, and trivially small percentage differences should not be narrated as meaningful findings even where a test is significant.
  4. [§3.3, §4.1.2, §4.3, Table 1] Headline subgroup claims rest on extremely sparse cells. Table 1: Gender 'Other' n=5, Oceania n=12, Africa n=22 (before the newcomer/practitioner split, so smaller in each analysis). Yet §4.1.2 draws Career conclusions from Female-vs-Other and Male-vs-Other contrasts, and regional claims (Africa vs America/Europe for Incentives, Reputation, Networking; RQ3 follower/project-age preferences) rest on cells of ~10–20. Kruskal–Wallis with such cells is unstable and the percentage differences (e.g., '56% more from Africa') are driven by single-digit counts. Report the exact n for every cell in Tables 3–17, and either merge, downweight, or drop contrasts involving 'Other' and Oceania.
  5. [§3.4 (Thematic analysis), §4.4] §3.4 states Cohen's κ was computed 'following mediation' between coders. Agreement measured after discrepancies have been resolved by discussion is not inter-rater reliability — it is agreement with oneself after consensus. Additionally, the second and third authors each coded a different half of the data, so no shared subset was independently coded by all pairs, making the two κ values (0.96, 0.83) non-comparable. Either recompute κ on independent pre-mediation coding of a common subset, or remove the κ values and describe the process as negotiated consensus. This is the sole quantitative reliability evidence for RQ4.
minor comments (8)
  1. [§4.4] Broken cross-reference: 'Table?? shows the key themes…' — the intended Table 19 is not linked.
  2. [Figure 3] Figure 3 caption reads 'Likelihood Ratio Analysis p-values Heat Maps', but the method used throughout is Kruskal–Wallis; reconcile the caption with the text.
  3. [Abstract vs §4.2] The abstract claims preferences for 'project age, development stage, and documentation quality vary based on specific motivations', but §4.2 reports no significant motivation differences for project age or stage among OSS practitioners. Adjust the abstract to match the RQ2 results.
  4. [Tables 3–17] Several tables (e.g., Table 3 'Other' gender row showing 0%/0%) have empty or near-empty cells. State cell sizes in all distribution tables so percentages are interpretable.
  5. [§4.1.1, Tables 4, 7] Define the ε² thresholds used for 'small/medium/large' and cite the source; the labels (e.g., 0.049 'small', 0.076 'medium') imply a specific convention that is never stated.
  6. [Throughout] Typos and consistency: 'McKight and Najab' (McKnight), 'Slighly', inconsistent 'P' vs 'p', and spacing artifacts around 'newcomers to OSS' throughout.
  7. [§3.3, §6] Round 1 of Prolific recruitment had a 22% pass rate (33/150). A sentence on what this selection implies for the sample (beyond the general external-validity paragraph) would strengthen §6, e.g., whether screener-failures differ demographically from passers.
  8. [References] A substantial share of the citations are arXiv preprints (e.g., Alebachew 2025, Sesari 2025, Song 2024, Cihan 2024). Where peer-reviewed versions exist they should be cited; otherwise note the status.

Circularity Check

0 steps flagged

Empirical survey correlations; no derivation reduces to its inputs by construction.

full rationale

This paper reports Kruskal-Wallis correlations and inductive themes from a one-shot survey of 208 practitioners. Motivations (7 Likert items drawn from prior OSS literature), project-characteristic preferences (8 items), and demographics are measured as separate questionnaire blocks; the statistical tests then relate those independently collected responses. There is no fitted parameter re-exported as a prediction, no self-definitional identity (X defined via Y then claimed to derive Y), no uniqueness theorem imported from the authors, and no ansatz smuggled in via self-citation. Self-citations (Nirmani et al. 2025 SLR; 2026 replication package) supply background and materials only and are not load-bearing for the reported correlations. Ordinary construct choices (item sets, merged sparse demographic bins) are not circularity. Score 0 is the correct outcome.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

Load-bearing commitments are methodological and construct-level: that the seven literature-derived motives and eight project traits adequately span selection drivers; that ordinal self-reports and KW rank tests answer the RQs; that merged demographic bins and the newcomer/experienced split are meaningful; and that screened Prolific/social-media respondents speak to OSS practice. No physical free parameters or new ontological entities.

free parameters (3)
  • Demographic bin cutpoints and merges = As in Table 1 (e.g., Age 18–24 n=48, 25–34 n=105, 35+ n=55)
    Age bands (18–24/25–34/35+), education collapses, SE experience labels, and role groupings were chosen/merged for sparsity; different cuts could change which KW contrasts appear.
  • Significance threshold and multiple-comparison policy = p_adj < 0.05
    α=0.05 with Bonferroni-adjusted post-hocs governs which correlations are reported as significant across many motive×factor and motive×trait families.
  • Seven motivation items and eight project-characteristic items = Learning, Helping, Enjoyment, Career, Networking, Reputation, Incentives; age, stage, contributors, followers, guideline
    Item set is a curated subset of highly cited motives/traits; inclusion/exclusion is an author design choice that defines the measured space of ‘preferences.’
axioms (4)
  • domain assumption Self-reported Likert motivations and stated project-characteristic preferences are valid indicators of real OSS project-selection drivers.
    Entire RQ1–RQ3 inference chain treats survey ratings as the phenomenon of interest; behavioral logs are not collected (Threats §6 recall/social-desirability).
  • standard math Kruskal–Wallis on ordinal Likert ranks with unequal group sizes appropriately tests demographic/motivation differences for these RQs.
    Invoked in §3.4 and Results; standard non-parametric choice but still assumes comparable ordinal interpretation across groups.
  • domain assumption Respondents who pass programming screening and self-report SE background represent the target population of potential/actual OSS contributors when split into newcomers vs experienced.
    Sampling frame is Prolific + LinkedIn convenience (§3.3); external validity threat acknowledged in §6.
  • ad hoc to paper The selected seven motivations and project traits from prior literature are sufficient to study ‘how contributors select OSS projects’ for recommender implications.
    §3.2.1 explicitly subsets motives/traits; authors note the set may not capture the full range (§6).

pith-pipeline@v1.2.0-grok45-kimik3 · 30452 in / 3413 out tokens · 69243 ms · 2026-07-31T00:34:17.984888+00:00 · methodology

0 comments
read the original abstract

Open source software (OSS) development continues to expand, yet software practitioners often struggle to select suitable projects, leading to inefficient onboarding and disengagement. Understanding how contributors select OSS projects is important for supporting contributors onboarding, engagement, and long-term participation within OSS communities. This study investigates contributors' project-selection preferences in OSS projects and examines how these preferences correlate with contributors' motivations and demographic backgrounds. Through an online survey of 208 practitioners, we found that demographic factors, such as age, gender, and the OSS role they held, significantly correlate with their motivations. Additionally, preferences for project characteristics such as project age, development stage, and documentation quality vary based on specific motivations. Importantly, our findings are presented through a comparative lens, analyzing the responses of newcomers to OSS and experienced OSS practitioners separately to uncover their distinct preferences. Lastly, we explore software practitioners' perspectives on how existing recommendation systems could better support project selection and align with their motivations. By disentangling the unique needs of newcomers to OSS and OSS practitioners, our findings provide insights for researchers, OSS project owners, and software practitioners to improve contributor onboarding, engagement, and retention, while also informing future project recommendation systems and improving the OSS ecosystem.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

130 extracted references · 1 canonical work pages

  1. [1]

    Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering , pages=

    Training the future workforce through task curation in an OSS ecosystem , author=. Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering , pages=

    2016

  2. [2]

    Annual review of sociology , volume=

    An introduction to categorical data analysis , author=. Annual review of sociology , volume=. 1996 , publisher=

    1996

  3. [3]

    AIP Conference Proceedings , volume=

    Applicability of G-test in analyzing categorical variables , author=. AIP Conference Proceedings , volume=. 2019 , organization=

    2019

  4. [4]

    2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pages=

    Do you really code? designing and evaluating screening questions for online surveys with programmers , author=. 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pages=. 2021 , organization=

    2021

  5. [5]

    Guide to advanced empirical software engineering , pages=

    Personal opinion surveys , author=. Guide to advanced empirical software engineering , pages=. 2008 , publisher=

    2008

  6. [6]

    Journal of Applied Sciences Research , volume=

    Comparison of Chi-Square and likelihood ratio Chi-Square tests: Power of test , author=. Journal of Applied Sciences Research , volume=

  7. [7]

    Visual Studio Code

    Microsoft. Visual Studio Code. 2025 , url =

    2025

  8. [8]

    TensorFlow: An Open Source Machine Learning Framework

    Google Brain Team. TensorFlow: An Open Source Machine Learning Framework. 2025 , url =

    2025

  9. [9]

    React: A JavaScript Library for Building User Interfaces

    Meta Open Source. React: A JavaScript Library for Building User Interfaces. 2025 , url =

    2025

  10. [10]

    Next.js: The React Framework for Production

    Vercel. Next.js: The React Framework for Production. 2025 , url =

    2025

  11. [11]

    Flutter: UI Toolkit for Building Natively Compiled Applications

    Flutter Team. Flutter: UI Toolkit for Building Natively Compiled Applications. 2025 , url =

    2025

  12. [12]

    Information & Management , volume=

    An empirical analysis of open source software developers’ motivations and continuance intentions , author=. Information & Management , volume=. 2007 , publisher=

    2007

  13. [13]

    Journal of comparative economics , volume=

    Intrinsic motivation in open source software development , author=. Journal of comparative economics , volume=. 2007 , publisher=

    2007

  14. [14]

    Why do people contribute to open source software?: the motivations of the different contributors to OSS projects, and how their participation is characterized , author=

  15. [15]

    MIS quarterly , pages=

    Carrots and rainbows: Motivation and social practice in open source software development , author=. MIS quarterly , pages=. 2012 , publisher=

    2012

  16. [16]

    Research policy , volume=

    Motivation of software developers in Open Source projects: an Internet-based survey of contributors to the Linux kernel , author=. Research policy , volume=. 2003 , publisher=

    2003

  17. [17]

    International Journal of Electronic Commerce , volume=

    Motivations in open source software communities: The mediating role of effort intensity and goal commitment , author=. International Journal of Electronic Commerce , volume=. 2009 , publisher=

    2009

  18. [18]

    2002 , note=

    Free/Libre and Open Source Software: Survey and Study , author=. 2002 , note=

    2002

  19. [19]

    Perspectives on Free and Open Source Software , editor=

    Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects , author=. Perspectives on Free and Open Source Software , editor=. 2005 , publisher=

    2005

  20. [20]

    European Management Review , volume=

    A fistful of dollars: Are financial rewards a suitable management practice for distributed models of innovation? , author=. European Management Review , volume=. 2011 , publisher=

    2011

  21. [21]

    The journal of industrial economics , volume=

    Some simple economics of open source , author=. The journal of industrial economics , volume=. 2002 , publisher=

    2002

  22. [22]

    Management science , volume=

    Understanding the motivations, participation, and performance of open source software developers: A longitudinal study of the Apache projects , author=. Management science , volume=. 2006 , publisher=

    2006

  23. [23]

    2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pages=

    The shifting sands of motivation: Revisiting what drives contributors in open source , author=. 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pages=. 2021 , organization=

    2021

  24. [24]

    2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pages=

    Leaving my fingerprints: Motivations and challenges of contributing to OSS for social good , author=. 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , pages=. 2021 , organization=

    2021

  25. [25]

    Information and software technology , volume=

    Motivation in Software Engineering: A systematic literature review , author=. Information and software technology , volume=. 2008 , publisher=

    2008

  26. [26]

    2013 , publisher=

    Peopleware: productive projects and teams , author=. 2013 , publisher=

    2013

  27. [27]

    Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: Forty four years of computer personnel research: achievements, challenges & the future , pages=

    An effort towards identifying occupational culture among information systems professionals , author=. Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: Forty four years of computer personnel research: achievements, challenges & the future , pages=

    2006

  28. [28]

    15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011) , pages=

    Motivation in software engineering: A systematic review update , author=. 15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011) , pages=. 2011 , organization=

    2011

  29. [29]

    25th International Conference on Software Engineering, 2003

    Toward an understanding of the motivation of open source software developers , author=. 25th International Conference on Software Engineering, 2003. Proceedings. , pages=. 2003 , organization=

    2003

  30. [30]

    Knowledge, Technology & Policy , volume=

    On the intrinsic and extrinsic motivation of free/libre/open source (FLOSS) developers , author=. Knowledge, Technology & Policy , volume=. 2006 , publisher=

    2006

  31. [31]

    How do intrinsic and extrinsic motivation correlate with each other in open source software development? , author=

  32. [32]

    Leadership and Motivation in Open Source Projects , author=

  33. [33]

    Information & Management , volume=

    Leadership characteristics and developers’ motivation in open source software development , author=. Information & Management , volume=. 2012 , publisher=

    2012

  34. [34]

    Research Policy , volume=

    What makes the right OSS contributor tick? Treatments to motivate high-skilled developers , author=. Research Policy , volume=. 2022 , publisher=

    2022

  35. [35]

    International journal of electronic commerce , volume=

    Working for free? Motivations for participating in open-source projects , author=. International journal of electronic commerce , volume=. 2002 , publisher=

    2002

  36. [36]

    2002 , publisher=

    Software engineering economics , author=. 2002 , publisher=

    2002

  37. [37]

    IEEE Software , volume=

    Problem programmers , author=. IEEE Software , volume=. 1998 , publisher=

    1998

  38. [38]

    Journal of Systems and Software , volume=

    What do software practitioners really think about project success: an exploratory study , author=. Journal of Systems and Software , volume=. 2005 , publisher=

    2005

  39. [39]

    2024 IEEE International Conference on Software Maintenance and Evolution (ICSME) , pages=

    Investigating Developers' Preferences for Learning and Issue Resolution Resources in the ChatGPT Era , author=. 2024 IEEE International Conference on Software Maintenance and Evolution (ICSME) , pages=. 2024 , organization=

    2024

  40. [40]

    Proceedings of the 2007 ACM SIGMIS CPR conference on Computer personnel research: The global information technology workforce , pages=

    Knowledge contribution in problem solving virtual communities: the mediating role of individual motivations , author=. Proceedings of the 2007 ACM SIGMIS CPR conference on Computer personnel research: The global information technology workforce , pages=

    2007

  41. [41]

    International Economics and Economic Policy , volume=

    Open source software: Motivation and restrictive licensing , author=. International Economics and Economic Policy , volume=. 2007 , publisher=

    2007

  42. [42]

    The Journal of Strategic Information Systems , volume=

    The attraction of contributors in free and open source software projects , author=. The Journal of Strategic Information Systems , volume=. 2013 , publisher=

    2013

  43. [43]

    Seventh European Conference onSoftware Maintenance and Reengineering, 2003

    Characteristics of open source projects , author=. Seventh European Conference onSoftware Maintenance and Reengineering, 2003. Proceedings. , pages=. 2003 , organization=

    2003

  44. [44]

    2015 IEEE/ACM 8th International Workshop on Cooperative and Human Aspects of Software Engineering , pages=

    Characteristics of sustainable oss projects: A theoretical and empirical study , author=. 2015 IEEE/ACM 8th International Workshop on Cooperative and Human Aspects of Software Engineering , pages=. 2015 , organization=

    2015

  45. [45]

    Information and Software Technology , volume=

    Exploring information from OSS repositories and platforms to support OSS selection decisions , author=. Information and Software Technology , volume=. 2018 , publisher=

    2018

  46. [46]

    Proceedings of the 8th ACM/IEEE international symposium on empirical software engineering and measurement , pages=

    The role of mentoring and project characteristics for onboarding in open source software projects , author=. Proceedings of the 8th ACM/IEEE international symposium on empirical software engineering and measurement , pages=

  47. [47]

    arXiv preprint arXiv:2410.02091 , year=

    The impact of generative AI on collaborative open-source software development: Evidence from GitHub Copilot , author=. arXiv preprint arXiv:2410.02091 , year=

    Pith/arXiv arXiv

  48. [48]

    arXiv preprint arXiv:2409.08379 , year=

    The impact of large language models on open-source innovation: Evidence from GitHub Copilot , author=. arXiv preprint arXiv:2409.08379 , year=

    Pith/arXiv arXiv

  49. [49]

    arXiv preprint arXiv:2501.13282 , year=

    Experience with GitHub Copilot for Developer Productivity at Zoominfo , author=. arXiv preprint arXiv:2501.13282 , year=

    Pith/arXiv arXiv

  50. [50]

    ACM Transactions on Software Engineering and Methodology , volume=

    Navigating the complexity of generative ai adoption in software engineering , author=. ACM Transactions on Software Engineering and Methodology , volume=. 2024 , publisher=

    2024

  51. [51]

    Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , pages=

    Stuck in the permissions with you: Developer & end-user perspectives on app permissions & their privacy ramifications , author=. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , pages=

    2023

  52. [52]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    Competencies for code review , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2023 , publisher=

    2023

  53. [53]

    arXiv preprint arXiv:2201.05348 , year=

    Software engineering user study recruitment on prolific: An experience report , author=. arXiv preprint arXiv:2201.05348 , year=

    Pith/arXiv arXiv

  54. [54]

    Information and Software Technology , year=

    A systematic literature review on task recommendation systems for crowdsourced software engineering , author=. Information and Software Technology , year=

  55. [55]

    Journal of Software: Evolution and Process , volume=

    CrowdAssist: A multidimensional decision support system for crowd workers , author=. Journal of Software: Evolution and Process , volume=. 2023 , publisher=

    2023

  56. [56]

    Information and Software Technology , pages=

    Software solutions for newcomers’ onboarding in software projects: A systematic literature review , author=. Information and Software Technology , pages=. 2024 , publisher=

    2024

  57. [57]

    2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) , pages=

    CodeCompass: NLP-Driven Navigation to Optimal Repositories , author=. 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) , pages=. 2024 , organization=

    2024

  58. [58]

    Information and Software Technology , volume=

    Multi-objective optimization and integrated indicator-driven two-stage project recommendation in time-dependent software ecosystem , author=. Information and Software Technology , volume=. 2024 , publisher=

    2024

  59. [59]

    2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) , pages=

    Personalized First Issue Recommender for Newcomers in Open Source Projects , author=. 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) , pages=. 2023 , organization=

    2023

  60. [60]

    IEEE Transactions on Software Engineering , volume=

    Off to a good start: Dynamic contribution patterns and technical success in an oss newcomer’s early career , author=. IEEE Transactions on Software Engineering , volume=. 2022 , publisher=

    2022

  61. [61]

    Journal of Software: Evolution and Process , volume=

    Understanding Remote Work Experience: Insights Into Well-Being , author=. Journal of Software: Evolution and Process , volume=. 2025 , publisher=

    2025

  62. [62]

    Mechanics Transport Communications-Academic journal, , volume=

    SOFTWARE PROJECTS SUCCESS: DEFINITION, METRICS AND PERCEPTIONS OF PRACTITIONERS & STAKEHOLDERS--AN EVALUATION , author=. Mechanics Transport Communications-Academic journal, , volume=

  63. [63]

    Proceedings of the 2023 12th International Conference on Software and Information Engineering , pages=

    A review of human factors in remote software project management: A progressive look at human based issues in remote software development environments , author=. Proceedings of the 2023 12th International Conference on Software and Information Engineering , pages=

    2023

  64. [64]

    Proceedings of the ACM on human-computer interaction , volume=

    Hidden figures: Roles and pathways of successful oss contributors , author=. Proceedings of the ACM on human-computer interaction , volume=. 2020 , publisher=

    2020

  65. [65]

    arXiv preprint arXiv:2403.08113 , year=

    Assessing the influence of toxic and gender discriminatory communication on perceptible diversity in oss projects , author=. arXiv preprint arXiv:2403.08113 , year=

    Pith/arXiv arXiv

  66. [66]

    Research Policy , volume=

    Open source software and global entrepreneurship , author=. Research Policy , volume=. 2023 , publisher=

    2023

  67. [67]

    Proceedings of the ACM on Human-Computer Interaction , volume=

    The signals that potential contributors look for when choosing open-source projects , author=. Proceedings of the ACM on Human-Computer Interaction , volume=. 2019 , publisher=

    2019

  68. [68]

    Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , pages=

    Predicting open source contributor turnover from value-related discussions: An analysis of GitHub issues , author=. Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , pages=

  69. [69]

    The International FLAIRS Conference Proceedings , volume=

    Recommendation System for Open Source Projects for Minimizing Abandonment , author=. The International FLAIRS Conference Proceedings , volume=

  70. [70]

    Proceedings of the 15th International Conference on Global Software Engineering , pages=

    TasRec: a framework for task recommendation in crowdsourcing , author=. Proceedings of the 15th International Conference on Global Software Engineering , pages=

  71. [71]

    Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=

    Do contributing files provide information about oss newcomers’ onboarding barriers? , author=. Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=

  72. [72]

    arXiv preprint arXiv:2502.18440 , year=

    The Introduction of README and CONTRIBUTING Files in Open Source Software Development , author=. arXiv preprint arXiv:2502.18440 , year=

    Pith/arXiv arXiv

  73. [73]

    Proceedings of the 44th International Conference on Software Engineering , pages=

    Recommending good first issues in github oss projects , author=. Proceedings of the 44th International Conference on Software Engineering , pages=

  74. [74]

    Proceedings of the 15th ACM/IEEE international symposium on Empirical Software Engineering and Measurement (ESEM) , pages=

    Characterizing and predicting good first issues , author=. Proceedings of the 15th ACM/IEEE international symposium on Empirical Software Engineering and Measurement (ESEM) , pages=

  75. [75]

    arXiv preprint arXiv:2411.01601 , year=

    Investigating the Impact of Interpersonal Challenges on Feeling Welcome in OSS , author=. arXiv preprint arXiv:2411.01601 , year=

    Pith/arXiv arXiv

  76. [76]

    arXiv preprint arXiv:2501.09493 , year=

    Evaluating Conversational Recommender Systems with Large Language Models: A User-Centric Evaluation Framework , author=. arXiv preprint arXiv:2501.09493 , year=

    arXiv

  77. [77]

    Mathematics , volume=

    Extracting Implicit User Preferences in Conversational Recommender Systems Using Large Language Models , author=. Mathematics , volume=. 2025 , publisher=

    2025

  78. [78]

    2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) , pages=

    A collaboration-aware approach to profiling developer expertise with cross-community data , author=. 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) , pages=. 2022 , organization=

    2022

  79. [79]

    Computer and Knowledge Engineering , volume=

    Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms , author=. Computer and Knowledge Engineering , volume=. 2024 , publisher=

    2024

  80. [80]

    IEEE Software , year=

    Psychological Safety in the Software Work Environment , author=. IEEE Software , year=

Showing first 80 references.