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arxiv: 2604.24478 · v1 · submitted 2026-04-27 · 💻 cs.HC · cs.SE

Putting a Face to the Issue: Fostering User Empathy of Open Source Software Developers With PersonaFlow

Boniface Bahati Tadjuidje , Jin L.C. Guo , Jinghui Cheng This is my paper

Pith reviewed 2026-05-08 02:13 UTC · model grok-4.3

classification 💻 cs.HC cs.SE
keywords userdevelopersissuepersonasthempersonaflowsoftwaretools
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The pith

PersonaFlow integrates generated user personas into OSS issue trackers to foster developer empathy.

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

Open source software developers often focus on technical details in issue trackers and miss user context. PersonaFlow generates editable personas from repository artifacts like issues and code to address this gap. In a study with 13 developers, most reported shifts in understanding users, with more than half changing their responses to be more empathetic or prioritized. The tool reveals two pathways to change: emotional connection to personas or pragmatic use for decision making. This matters because it offers a scalable method to humanize efficiency-driven development processes without requiring UX expertise.

Core claim

By generating and displaying editable user personas alongside issue reports in OSS repositories, PersonaFlow leads developers to alter their understanding of users and modify their responses accordingly, as evidenced by shifts in language, explanations, and priority ratings in a user study with 13 participants.

What carries the argument

PersonaFlow, which automatically creates editable personas from OSS repository artifacts and integrates them into the issue tracking workflow.

Load-bearing premise

The shifts observed in the developers' understanding and responses are caused by the personas rather than by participating in the study itself.

What would settle it

Repeating the study with a control group that does not see the personas but performs the same tasks would show if changes occur without PersonaFlow.

Figures

Figures reproduced from arXiv: 2604.24478 by Boniface Bahati Tadjuidje, Jinghui Cheng, Jin L.C. Guo.

Figure 1
Figure 1. Figure 1: Repository analysis and persona generation workflow. Users input the repository URL (A), configure generation view at source ↗
Figure 2
Figure 2. Figure 2: Persona management and analytics workflow. The repository dashboard (A) shows three main functions: View view at source ↗
Figure 3
Figure 3. Figure 3: Issue browsing and management workflow. A toggle switches between GitHub-style list view (A) and persona-grouped view at source ↗
Figure 4
Figure 4. Figure 4: System architecture of PersonaFlow, illustrating how the three user workflows connect to backend processing view at source ↗
read the original abstract

Open-source software (OSS) developers often struggle to understand and respond to user context, while existing tools, such as issue trackers (for handling bugs, requests, and feedback), largely focus on technical discussion. Although personas could help, limited resources and UX expertise make them hard to scale. We present PersonaFlow, a tool that generates editable user personas from OSS repository artifacts and integrates them alongside issue reports. In a user study with 13 OSS developers, most reported shifts in how they understood users, and more than half modified their responses by adding empathetic language, tailoring explanations, or raising priority ratings. We found two pathways to this change: some connected emotionally to personas as people, while others used them pragmatically for triaging. Both appeared to lead to more user-centered behavior. We contribute design implications for persona-based tools relevant to OSS and other contexts where efficiency-driven systems or workflows obscure valuable human elements.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces PersonaFlow, a tool that automatically generates editable user personas from OSS repository artifacts (e.g., issues, commits) and integrates them directly into issue-tracking interfaces. In a qualitative user study with 13 OSS developers, the authors report that most participants experienced shifts in how they understood users, and more than half altered their triage responses by adding empathetic language, tailoring explanations, or raising priority ratings. Two pathways to change are identified: emotional connection to personas as people and pragmatic use for triaging. The work contributes design implications for persona-based tools in efficiency-driven contexts like OSS development.

Significance. If the causal attribution to PersonaFlow holds, the work offers a practical approach to injecting user context into technical OSS workflows where it is often absent. The dual-pathway finding (emotional vs. pragmatic) provides a useful distinction for future tool design. The contribution is exploratory and could inform HCI research on empathy tools, but the small sample and single-condition design limit claims of generalizability or robust effectiveness.

major comments (2)
  1. [§4 (User Study)] §4 (User Study): The central claim that PersonaFlow causes shifts in developer understanding and response behavior rests on a single-condition study in which all 13 participants triaged issues while viewing the generated personas. No baseline phase, control arm, or counterbalanced condition is described in which the same developers responded to identical issues without personas. Consequently, the reported changes (e.g., added empathetic language or priority adjustments) cannot be isolated from demand characteristics, explicit prompting to consider users, or the study setting itself.
  2. [§5 (Results)] §5 (Results): The paper states that 'more than half modified their responses' and identifies two pathways, yet provides no details on the response-coding protocol, inter-rater reliability, quantitative measures of change, or concrete before/after examples. This absence makes it difficult to evaluate the magnitude or reliability of the observed shifts that underpin the abstract and conclusion.
minor comments (1)
  1. [Abstract and §6] The abstract and §6 (Discussion) could more explicitly qualify the exploratory nature of the findings and the absence of a control condition to avoid overstatement of causality.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of our study design and reporting. We address each major comment below and describe the revisions we will make to improve clarity and transparency.

read point-by-point responses

  1. Referee: §4 (User Study): The central claim that PersonaFlow causes shifts in developer understanding and response behavior rests on a single-condition study in which all 13 participants triaged issues while viewing the generated personas. No baseline phase, control arm, or counterbalanced condition is described in which the same developers responded to identical issues without personas. Consequently, the reported changes (e.g., added empathetic language or priority adjustments) cannot be isolated from demand characteristics, explicit prompting to consider users, or the study setting itself.

    Authors: We agree that the single-condition design limits causal attribution and that observed shifts cannot be fully isolated from study context or demand effects. The study was intentionally exploratory and qualitative, focusing on how developers experienced and used the integrated personas during realistic triage tasks, with data collected via think-aloud protocols and semi-structured interviews. We did not include a no-persona baseline because the primary goal was to examine the tool's integration into existing workflows rather than to conduct a controlled effectiveness trial. In the revision, we will explicitly qualify all claims to reflect self-reported and observed associations with PersonaFlow use, remove any phrasing that could imply direct causation, and add a dedicated limitations subsection discussing the absence of a control condition, potential demand characteristics, and implications for interpreting the results. revision: partial

  2. Referee: §5 (Results): The paper states that 'more than half modified their responses' and identifies two pathways, yet provides no details on the response-coding protocol, inter-rater reliability, quantitative measures of change, or concrete before/after examples. This absence makes it difficult to evaluate the magnitude or reliability of the observed shifts that underpin the abstract and conclusion.

    Authors: We will revise the Methods and Results sections to provide a full description of the qualitative analysis process, including the coding scheme used to identify modifications in triage responses (e.g., additions of empathetic language, priority changes), how the two pathways were derived, and any inter-rater reliability checks performed. We will also include anonymized concrete examples of before-and-after responses where participants consented. Although the study is qualitative and does not emphasize quantitative metrics, we acknowledge that greater detail on the analysis will strengthen the paper and allow readers to better assess the findings. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical user study with direct behavioral observations

full rationale

The paper reports results from a single-condition user study (n=13) in which OSS developers triaged issues with and without exposure to generated personas. All load-bearing claims rest on observed changes in self-reported understanding and response modifications (e.g., added empathetic language, priority shifts). No equations, fitted parameters, model predictions, or self-citations are invoked to derive the central findings; the evidence is the raw study data itself. No self-definitional loops, renamed known results, or ansatz smuggling appear. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that personas generated from repository artifacts can meaningfully represent user contexts and that qualitative self-reports plus observed response changes validly indicate increased empathy and user-centered behavior.

axioms (2)
  • domain assumption Personas generated automatically from OSS repository artifacts can accurately capture relevant user contexts and needs
    The tool's value depends on this premise being sufficiently true for the generated personas to influence developer thinking.
  • domain assumption Self-reported shifts and modifications in issue responses reflect genuine increases in empathy rather than study artifacts
    The interpretation of the user study outcomes relies on this without additional validation measures described.

pith-pipeline@v0.9.0 · 5467 in / 1469 out tokens · 49723 ms · 2026-05-08T02:13:30.091416+00:00 · methodology

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Reference graph

Works this paper leans on

97 extracted references · 47 canonical work pages

  1. [1]

    Jodi Aronson. 1995. A Pragmatic View of Thematic Analysis.The Qualitative Report2, 1 (1995), 1–3. doi:10.46743/2160-3715/1995.2069

    work page doi:10.46743/2160-3715/1995.2069 1995

  2. [2]

    Guo, and Jinghui Cheng

    Deeksha Arya, Wenting Wang, Jin L.C. Guo, and Jinghui Cheng. 2019. Analysis and Detection of Information Types of Open Source Software Issue Discussions. In2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). 454–464. doi:10.1109/ICSE.2019.00058

    work page doi:10.1109/icse.2019.00058 2019

  3. [3]

    Sogol Balali, Igor Steinmacher, Umayal Annamalai, Anita Sarma, and Marco Au- relio Gerosa. 2018. Newcomers’ Barriers. . . Is That All? An Analysis of Mentors’ and Newcomers’ Barriers in OSS Projects.Comput. Supported Coop. Work27, 3–6 (dec 2018), 679–714. doi:10.1007/s10606-018-9310-8

    work page doi:10.1007/s10606-018-9310-8 2018

  4. [4]

    Olga Baysal, Reid Holmes, and Michael W. Godfrey. 2014. No issue left behind: reducing information overload in issue tracking. InProceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering(Hong Kong, China)(FSE 2014). Association for Computing Machinery, New York, NY, USA, 666–677. doi:10.1145/2635868.2635887 Putting...

    work page doi:10.1145/2635868.2635887 2014

  5. [5]

    Bennett and Daniela K

    Cynthia L. Bennett and Daniela K. Rosner. 2019. The Promise of Empathy: Design, Disability, and Knowing the "Other". InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. doi:10.1145/3290605.3300528

    work page doi:10.1145/3290605.3300528 2019

  6. [6]

    Carmel and R

    E. Carmel and R. Agarwal. 2001. Tactical approaches for alleviating distance in global software development.IEEE Software18, 2 (2001), 22–29. doi:10.1109/52. 914734

    work page doi:10.1109/52 2001

  7. [7]

    1999.The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity

    Alan Cooper. 1999.The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity. Sams Publishing, Indianapolis, IN

    1999

  8. [8]

    Catherine Durnell Cramton. 2001. The Mutual Knowledge Problem and Its Consequences for Dispersed Collaboration.Organization Science12, 3 (June 2001), 346–371. doi:10.1287/orsc.12.3.346.10098

    work page doi:10.1287/orsc.12.3.346.10098 2001

  9. [9]

    McNeese, Matthew J

    Wen Duan, Christopher Flathmann, Nathan J. McNeese, Matthew J. Scalia, Ruihao Zhang, Jamie Gorman, Guo Freeman, Shiwen Zhou, Allyson I. Hauptman, and Xiaoyun Yin. 2025. Trusting Autonomous Teammates in Human-AI Teams - A Literature Review. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI ’25). Association...

    work page doi:10.1145/3706598.3713527 2025

  10. [10]

    Mica R. Endsley. 1995. Toward a Theory of Situation Awareness in Dynamic Systems.Human Factors37, 1 (1995), 32–64. doi:10.1518/001872095779049543

    work page doi:10.1518/001872095779049543 1995

  11. [11]

    Shamal Faily and Ivan Flechais. 2011. Persona cases: a technique for grounding personas. InProceedings of the SIGCHI Conference on Human Factors in Com- puting Systems(Vancouver, BC, Canada)(CHI ’11). Association for Computing Machinery, New York, NY, USA, 2267–2270. doi:10.1145/1978942.1979274

    work page doi:10.1145/1978942.1979274 2011

  12. [12]

    Joseph Feller, Brian Fitzgerald, Scott A Hissam, and Karim R Lakhani. 2005. Perspectives on free and open source software. MIT Press, Cambridge, Mass. doi:10. 7551/mitpress/5326.001.0001

    2005

  13. [13]

    Isabella Ferreira, Bram Adams, and Jinghui Cheng. 2022. How heated is it? understanding GitHub locked issues. InProceedings of the 19th International Conference on Mining Software Repositories(Pittsburgh, Pennsylvania)(MSR ’22). Association for Computing Machinery, New York, NY, USA, 309–320. doi:10. 1145/3524842.3527957

    work page arXiv 2022

  14. [14]

    Shut the f**k up

    Isabella Ferreira, Jinghui Cheng, and Bram Adams. 2021. The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions. Proc. ACM Hum.-Comput. Interact.5, CSCW2, Article 353 (Oct. 2021), 35 pages. doi:10.1145/3479497

    work page doi:10.1145/3479497 2021

  15. [15]

    Hashini Gunatilake, John Grundy, Rashina Hoda, and Ingo Mueller. 2024. Enablers and Barriers of Empathy in Software Developer and User Interactions: A Mixed Methods Case Study.ACM Trans. Softw. Eng. Methodol.33, 4, Article 109 (apr 2024), 41 pages. doi:10.1145/3641849

    work page doi:10.1145/3641849 2024

  16. [16]

    Sami Uddin, Jinghui Cheng, and Jin L

    Jazlyn Hellman, Jiahao Chen, Md. Sami Uddin, Jinghui Cheng, and Jin L. C. Guo

  17. [17]

    InProceedings of the 15th International Conference on Cooperative and Human Aspects of Software Engineering(Pittsburgh, Pennsylvania)(CHASE ’22)

    Characterizing user behaviors in open-source software user forums: an empirical study. InProceedings of the 15th International Conference on Cooperative and Human Aspects of Software Engineering(Pittsburgh, Pennsylvania)(CHASE ’22). Association for Computing Machinery, New York, NY, USA, 46–55. doi:10. 1145/3528579.3529178

    work page arXiv

  18. [18]

    Jazlyn Hellman, Itai Epstein, Jinghui Cheng, and Jin L.C. Guo. 2025. ’Ohhh, he’s the boss!’: Unpacking Power Dynamics Among Developers, Designers, and End-Users in FLOSS Usability.Proc. ACM Hum.-Comput. Interact.9, 7, Article CSCW262 (Oct. 2025), 30 pages. doi:10.1145/3757443

    work page doi:10.1145/3757443 2025

  19. [19]

    Hunt, Hannah S

    Linda M. Hunt, Hannah S. Bell, Allison M. Baker, and Heather A. Howard. 2017. Electronic Health Records and the Disappearing Patient.Medical Anthropology Quarterly31, 3 (May 2017), 403–421. doi:10.1111/maq.12375

    work page doi:10.1111/maq.12375 2017

  20. [20]

    Jansen, Joni O

    Bernard J. Jansen, Joni O. Salminen, and Soon-gyo Jung. 2020. Data-Driven Per- sonas for Enhanced User Understanding: Combining Empathy with Rationality for Better Insights to Analytics.Data and Information Management4, 1 (2020), 1–17. doi:10.2478/dim-2020-0005

    work page doi:10.2478/dim-2020-0005 2020

  21. [21]

    Soon-Gyo Jung, Joni Salminen, Kholoud Khalil Aldous, and Bernard J. Jansen

  22. [22]

    doi:10.1016/j.ijhcs.2025.103445

    PersonaCraft: Leveraging language models for data-driven persona devel- opment.International Journal of Human-Computer Studies197 (2025), 103445. doi:10.1016/j.ijhcs.2025.103445

    work page doi:10.1016/j.ijhcs.2025.103445 2025

  23. [23]

    Finding the Magic Sauce

    Mitra Lashkari and Jinghui Cheng. 2023. “Finding the Magic Sauce”: Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 868, 16 pages. doi:10.114...

    work page doi:10.1145/3544548.3581548 2023

  24. [24]

    Shuai Ma, Ying Lei, Xinru Wang, Chengbo Zheng, Chuhan Shi, Ming Yin, and Xiaojuan Ma. 2023. Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision- Making. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Association for...

    work page doi:10.1145/3544548.3581058 2023

  25. [25]

    Nicola Marsden and Maren Haag. 2016. Stereotypes and Politics: Reflections on Personas. InProceedings of the 2016 CHI Conference on Human Factors in Com- puting Systems(San Jose, California, USA)(CHI ’16). Association for Computing Machinery, New York, NY, USA, 4017–4031. doi:10.1145/2858036.2858151

    work page doi:10.1145/2858036.2858151 2016

  26. [26]

    Tara Matthews, Tejinder Judge, and Steve Whittaker. 2012. How do designers and user experience professionals actually perceive and use personas?. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Austin, Texas, USA)(CHI ’12). Association for Computing Machinery, New York, NY, USA, 1219–1228. doi:10.1145/2207676.2208573

    work page doi:10.1145/2207676.2208573 2012

  27. [27]

    Integrity-based Explanations for Fostering Appropriate Trust in AI Agents

    Siddharth Mehrotra, Carolina Centeio Jorge, Catholijn M. Jonker, and Myrthe L. Tielman. 2023. Integrity-based Explanations for Fostering Appropriate Trust in AI Agents.ACM Transactions on Interactive Intelligent Systems13, 4, Article 28 (2023), 39 pages. doi:10.1145/3610578

    work page doi:10.1145/3610578 2023

  28. [28]

    Did you miss my comment or what?

    Courtney Miller, Sophie Cohen, Daniel Klug, Bogdan Vasilescu, and Christian Kästner. 2022. "Did you miss my comment or what?": understanding toxicity in open source discussions. InProceedings of the 44th International Conference on Software Engineering(Pittsburgh, Pennsylvania)(ICSE ’22). Association for Com- puting Machinery, New York, NY, USA, 710–722. ...

    work page doi:10.1145/3510003.3510111 2022

  29. [29]

    Timothy Neate, Aikaterini Bourazeri, Abi Roper, Simone Stumpf, and Stephanie Wilson. 2019. Co-Created Personas: Engaging and Empowering Users with Diverse Needs Within the Design Process. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1...

    work page doi:10.1145/3290605.3300880 2019

  30. [30]

    I Don’t Want to Feel Like I’m Working in a 1960s Factory

    Sangeetha Palani, David Ledo, George Fitzmaurice, and Fraser Anderson. 2022. “I Don’t Want to Feel Like I’m Working in a 1960s Factory”: The Practitioner Perspective on Creativity Support Tool Adoption. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New Y...

    work page doi:10.1145/3491102.3501933 2022

  31. [31]

    Monika Pröbster, Mirza Ehsanul Haque, and Nicola Marsden. 2018. Perceptions of Personas: The Role of Instructions. In2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)(Stuttgart, Germany). 1–8. doi:10.1109/ICE.2018.8436339

    work page doi:10.1109/ice.2018.8436339 2018

  32. [32]

    John Pruitt and Jonathan Grudin. 2003. Personas: practice and theory. InPro- ceedings of the 2003 Conference on Designing for User Experiences(San Francisco, California)(DUX ’03). Association for Computing Machinery, New York, NY, USA, 1–15. doi:10.1145/997078.997089

    work page doi:10.1145/997078.997089 2003

  33. [33]

    Abdul Razzaq, Jim Buckley, Qin Lai, Tingting Yu, and Goetz Botterweck. 2024. A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers’ Productivity: Factors, Practices, and Recommendations.Comput. Surveys57, 3 (2024), 1–46. doi:10.1145/3687299

    work page doi:10.1145/3687299 2024

  34. [34]

    Jansen, Jisun An, Haewoon Kwak, and Soon-gyo Jung

    Joni Salminen, Bernard J. Jansen, Jisun An, Haewoon Kwak, and Soon-gyo Jung

  35. [35]

    doi:10.21153/psj2018vol4no2art737

    Are personas done? Evaluating their usefulness in the age of digital analytics.Persona Studies4, 2 (2018), 47–65. doi:10.21153/psj2018vol4no2art737

    work page doi:10.21153/psj2018vol4no2art737 2018

  36. [36]

    Joni Salminen, Chang Liu, Wenjing Pian, Jianxing Chi, Essi Häyhänen, and Bernard J Jansen. 2024. Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona Descriptions. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA)(CHI ’24). Association for Computing...

    work page doi:10.1145/3613904.3642036 2024

  37. [37]

    Submitting surveys via a conversational interface: An evalua- tion of user acceptance and approach effectiveness

    Joni Salminen, João M. Santos, Soon-gyo Jung, Motahhare Eslami, and Bernard J. Jansen. 2020. Persona Perception Scale: Development and Validation of an Instrument for Evaluating Individuals’ Perceptions of Personas.International Journal of Human-Computer Studies141 (2020), 102437. doi:10.1016/j.ijhcs.2020. 102437

    work page doi:10.1016/j.ijhcs.2020 2020

  38. [38]

    Arghavan Sanei and Jinghui Cheng. 2024. Characterizing Usability Issue Discus- sions in Open Source Software Projects.Proc. ACM Hum.-Comput. Interact.8, CSCW1, Article 30 (April 2024), 26 pages. doi:10.1145/3637307

    work page doi:10.1145/3637307 2024

  39. [39]

    Hedderich, Bartłomiej Jakub Rey, Andrés Lucero, and Antti Oulasvirta

    Joongi Shin, Michael A. Hedderich, Bartłomiej Jakub Rey, Andrés Lucero, and Antti Oulasvirta. 2024. Understanding Human-AI Workflows for Generating Personas. InProceedings of the 2024 ACM Designing Interactive Systems Conference (Copenhagen, Denmark)(DIS ’24). Association for Computing Machinery, New York, NY, USA, 757–781. doi:10.1145/3643834.3660729

    work page doi:10.1145/3643834.3660729 2024

  40. [40]

    Atefeh Shokrizadeh, Boniface Bahati Tadjuidje, Shivam Kumar, Sohan Kamble, and Jinghui Cheng. 2025. Dancing With Chains: Ideating Under Constraints With UIDEC in UI/UX Design. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, Article 1106, 23 pages. doi:10.1145...

    work page doi:10.1145/3706598.3713785 2025

  41. [41]

    Jaisie Sin, R. L. Franz, Cosmin Munteanu, and Barbara Barbosa Neves. 2021. Dig- ital Design Marginalization: New Perspectives on Designing Inclusive Interfaces. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan)(CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 348, 13 pages. doi:10.114...

    work page doi:10.1145/3411764.3445180 2021

  42. [42]

    Igor Steinmacher, Christoph Treude, and Marco Aurelio Gerosa. 2019. Let Me In: Guidelines for the Successful Onboarding of Newcomers to Open Source Projects. IEEE Software36, 4 (2019), 41–49. doi:10.1109/MS.2018.110162131

    work page doi:10.1109/ms.2018.110162131 2019

  43. [43]

    Mojtaba Vaismoradi, Hannele Turunen, and Terese Bondas. 2013. Content analy- sis and thematic analysis: Implications for conducting a qualitative descriptive study.Nursing & Health Sciences15, 3 (2013), 398–405. doi:10.1111/nhs.12048

    work page doi:10.1111/nhs.12048 2013

  44. [44]

    Oleksandra Vereschak, Fatemeh Alizadeh, Gilles Bailly, and Baptiste Caramiaux

  45. [45]

    Slota, Sherri R

    Trust in AI-assisted Decision Making: Perspectives from Those Behind the DIS ’26, June 13–17, 2026, Singapore, Singapore Boniface Bahati Tadjuidje, Jin L.C. Guo, and Jinghui Cheng System and Those for Whom the Decision is Made. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA) (CHI ’24). Association for Comp...

    work page doi:10.1145/3613904.3642018 2026

  46. [46]

    Wenting Wang, Deeksha Arya, Nicole Novielli, Jinghui Cheng, and Jin L.C. Guo

  47. [47]

    InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’20)

    ArguLens: Anatomy of Community Opinions On Usability Issues Using Argumentation Models. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. doi:10.1145/3313831.3376218

    work page doi:10.1145/3313831.3376218 2020

  48. [48]

    Wenting Wang, Jinghui Cheng, and Jin L.C. Guo. 2022. How Do Open Source Software Contributors Perceive and Address Usability?: Valued Factors, Practices, and Challenges.IEEE Software39, 1 (2022), 76–83. doi:10.1109/MS.2020.3009514

    work page doi:10.1109/ms.2020.3009514 2022

  49. [49]

    Yi Wang, Chetan Arora, Xiao Liu, Thuong Hoang, Vasudha Malhotra, Ben Cheng, and John Grundy. 2024. Who Uses Personas in Requirements Engineering: The Practitioners’ Perspective.Information and Software Technology169 (2024), 107414. doi:10.1016/j.infsof.2024.107414

    work page doi:10.1016/j.infsof.2024.107414 2024

  50. [50]

    Mairieli Wessel, Igor Wiese, Igor Steinmacher, and Marco A. Gerosa. 2021. Don’t Disturb Me: Challenges of Interacting with Software Bots on Open Source Soft- ware Projects.Proceedings of the ACM on Human-Computer Interaction5, CSCW1, Article 97 (2021), 21 pages. doi:10.1145/3476042

    work page doi:10.1145/3476042 2021

  51. [51]

    Angie Zhang, Alexander Boltz, Chun Wei Wang, and Min Kyung Lee. 2022. Al- gorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA, USA)(CHI ’22). As- sociation for Computing Machinery, New York, NY, USA, Article 14, 2...

    work page doi:10.1145/3491102.3501866 2022

  52. [52]

    Xishuo Zhang, Lin Liu, Yi Wang, Xiao Liu, Hailong Wang, Chetan Arora, Haichao Liu, Weijia Wang, and Thuong Hoang. 2024. Auto-Generated Personas: Enhanc- ing User-centered Design Practices among University Students. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems(Hon- olulu, HI, USA)(CHI EA ’24). Association for Computing M...

    work page doi:10.1145/3613905.3651043 2024

  53. [53]

    Gireen Naidu, Tranos Zuva, and Elias Mmbongeni Sibanda

    Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy. 2020. Effect of Confi- dence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making. InProceedings of the 2020 Conference on Fairness, Accountability, and Transparency(Barcelona, Spain)(FAT* ’20). Association for Computing Machin- ery, New York, NY, USA, 295–305. doi:10.1145/...

    work page doi:10.1145/3351095.3372852 2020

  54. [54]

    Zihe H Zhao. 2023. The Distribution and Disengagement of Women Contributors in Open-Source: 2008–2021. InProceedings of the 45th International Conference on Software Engineering: Companion Proceedings(Melbourne, Victoria, Australia) (ICSE ’23). IEEE Press, 305–307. doi:10.1109/ICSE-Companion58688.2023.00082 A LLM Prompts This appendix presents the LLM pro...

    work page doi:10.1109/icse-companion58688.2023.00082 2023

  55. [55]

    Is it internal (relative path in repo) or external?

  56. [56]

    What type of end-user information might it contain?

  57. [57]

    internal_links

    How valuable is it for understanding user needs, behaviors, and use cases? Think about: - WHO are the end users? (their roles, backgrounds, needs) - WHAT do they use this software for? - HOW do they learn and get support? - WHERE do they discuss their experiences? Be smart about identifying: - Product homepages often have user testimonials and use cases -...

    2026

  58. [58]

    User Types: Who uses this software? (roles, backgrounds, skill levels)

  59. [59]

    Use Cases: What do they use it for? (specific tasks, workflows)

  60. [60]

    User Needs: What problems does it solve for them?

  61. [61]

    Pain Points: What challenges do users face?

  62. [62]

    user_types

    Community: How do users get support and connect? Return JSON in the format below: {"user_types": ["list of identified user types with descriptions"], "primary_use_cases": ["main ways users interact with the software"], "user_needs": ["problems it solves, value it provides"], "pain_points": ["challenges or frustrations users experience"], "community_insigh...

  63. [63]

    Specific product features and capabilities that users interact with

  64. [64]

    Common workflows and use cases

  65. [65]

    Integration points with other tools

  66. [66]

    Performance or usability constraints

  67. [67]

    Analyze the following repository content to extract domain insights for persona generation: Repository Content: [README and additional context] Extract the following information:

    Target user segments and their distinct needs A.3.2 User Prompt. Analyze the following repository content to extract domain insights for persona generation: Repository Content: [README and additional context] Extract the following information:

  68. [68]

    Domain Summary (What & Why): - What is this product/tool/service? - What real-world problem does it solve? - What is the primary use case and context?

  69. [69]

    Key Features (How): - List the SPECIFIC features and capabilities users interact with - For each feature, explain the user workflow and use case - Note performance characteristics (speed, efficiency, limitations) - Identify integration capabilities with other tools

  70. [70]

    User Evidence (Who): - Extract any mentions of user types, roles, or segments - Identify different ways people might use this product

  71. [71]

    domain_summary

    Behavioral Insights: - What tasks are users trying to accomplish? - What friction points or challenges are mentioned? - What motivates users to adopt this solution? Focus on concrete evidence over assumptions. Return JSON in the format below: {"domain_summary": "Clear description of what this is and why it matters", "key_features": [{"name": "Feature Name...

  72. [72]

    Grounded in domain analysis data and SPECIFIC to the product

  73. [73]

    Diverse in demographics, backgrounds, and perspectives

  74. [74]

    Focused on behaviors and needs, not demographics

  75. [75]

    Free from bias and stereotypes

  76. [76]

    Guo, and Jinghui Cheng CRITICAL: Every goal and pain point MUST directly relate to using THIS SPECIFIC product/tool, not generic professional challenges

    Useful for product decisions with clear feature connections DIS ’26, June 13–17, 2026, Singapore, Singapore Boniface Bahati Tadjuidje, Jin L.C. Guo, and Jinghui Cheng CRITICAL: Every goal and pain point MUST directly relate to using THIS SPECIFIC product/tool, not generic professional challenges. A.4.2 User Prompt. Based on this domain analysis, create [N...

    2026

  77. [77]

    Consider: - Usage Context: Internal tool vs

    True Diversity - Go beyond job titles. Consider: - Usage Context: Internal tool vs. customer-facing vs. embedded - Interaction Mode: GUI users vs. API users vs. both - Frequency: Daily power users vs. occasional vs. one-time setup - Technical Spectrum: No-code -> Low-code -> Full developers - Geographic & Cultural: Global representation - Company Size: Fr...

  78. [78]

    Feature Coverage - Ensure personas collectively cover ALL features: - Core functionality (main workflows) - Advanced features (power user capabilities) - Integration features (connections to other tools) - Collaboration features (sharing, permissions) - API/Developer features (SDKs, webhooks, extensions) - Admin features (user management, security, compliance)

  79. [79]

    Reduce API integration time from 2 days to 2 hours

    Realistic Goals & Pain Points: GOALS - Mix different types: a) Feature-Specific: "Reduce API integration time from 2 days to 2 hours" b) Workflow/Process: "Streamline onboarding by embedding chat widget" c) Business Outcome: "Decrease support ticket volume by 40%" PAIN POINTS - Include various friction types: a) Missing Features: "Can’t share clickable li...

  80. [80]

    personas

    Confidence Scoring (0.0-1.0): - High (0.8-1.0): Strong evidence in domain analysis - Medium (0.6-0.79): Reasonable assumptions - Low (0.4-0.59): Speculative but plausible Return JSON in the format below: {"personas": [{"name": "Realistic name", "age": 25-65, "occupation": "Specific role and company type", "location": "City, Country (diverse locations)", "...

    2026

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