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arxiv: 2606.20935 · v1 · pith:BAADYXWPnew · submitted 2026-06-18 · 💻 cs.SE

Stakeholder Criteria in Technical Debt Decision-Making: A Practitioner-Informed Taxonomy

Joao Pedro Bittencourt , Rita Suzana Pitangueira Maciel This is my paper

Pith reviewed 2026-06-26 16:03 UTC · model grok-4.3

classification 💻 cs.SE
keywords technical debtstakeholder criteriadecision taxonomyacquisition and repaymentsoftware engineeringqualitative studypractitioner interviewsconceptual model
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The pith

A taxonomy from practitioner interviews shows stakeholder criteria for technical debt fall into six families that function as permissions when acquiring debt and authorizations when repaying it.

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

The paper establishes a unified taxonomy for the varied criteria software practitioners weigh when deciding on technical debt. Drawing from interviews, it groups these into six families and shows that the same families serve different roles: they justify shortcuts under pressure during acquisition but justify time and resource allocation during repayment. A sympathetic reader would care because scattered reports of individual factors like deadlines or team effects become comparable across projects and organizations. The conceptual model explains how criteria turn actionable through interpretation, translation between technical and business views, and organizational acceptance.

Core claim

The resulting taxonomy comprises six families: stakeholder-facing value; delivery and resource pressure; technical integrity and systemic risk; decision basis and epistemic style; governance and legitimation; and human and team sustainability. Acquisition and repayment share taxonomic families but differ in decision function. In acquisition, criteria often operate as permission mechanisms that make shortcuts acceptable under current constraints. In repayment, criteria operate as authorization mechanisms that justify allocating time and resources to address existing debt. The conceptual model further shows how criteria become actionable through stakeholder interpretation, translation across t

What carries the argument

The six-family taxonomy of stakeholder criteria for technical debt decisions, which organizes heterogeneous factors and distinguishes their roles as permission mechanisms versus authorization mechanisms.

If this is right

  • The same six families apply whether practitioners are deciding to acquire or repay technical debt.
  • Criteria act as permission mechanisms that accept shortcuts under delivery or resource constraints during acquisition.
  • Criteria act as authorization mechanisms that justify committing resources to fix debt during repayment.
  • The taxonomy supplies a structure for classifying decision contexts and comparing them across projects.
  • Criteria reach decisions through processes of stakeholder interpretation, translation between perspectives, and legitimation.

Where Pith is reading between the lines

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

  • Teams could adopt the six families as a checklist to ensure debt discussions address value, risk, governance, and sustainability factors together.
  • The permission-versus-authorization distinction suggests management tools should treat acquisition and repayment workflows as distinct rather than symmetric.
  • Testing the taxonomy in additional countries or project types could show whether new families emerge or the existing ones need refinement.
  • The model of interpretation and legitimation may apply to other engineering decisions where short-term pressures conflict with long-term system integrity.

Load-bearing premise

The criteria extracted and consolidated from 11 interviews in one country form a stable, generalizable taxonomy usable to classify and compare technical debt decision contexts across other settings without substantial revision.

What would settle it

A replication study in a different country or industry that identifies major decision criteria outside the six families or shows the permission-authorization distinction does not hold would require revising or discarding the taxonomy.

Figures

Figures reproduced from arXiv: 2606.20935 by Joao Pedro Bittencourt, Rita Suzana Pitangueira Maciel.

Figure 1
Figure 1. Figure 1: Overview of the taxonomy process. with users, clients, sponsors, and market value were grouped under stakeholder-facing value. Step 3: Family construction. We organized criteria into high￾level families through an iterative process, ensuring each family covered both acquisition and repayment contexts and represented a coherent concern. Step 4: Cross-decision stress test. We checked whether each family coul… view at source ↗
Figure 2
Figure 2. Figure 2: Conceptual model of stakeholder criteria mobilization in TD decision-making. The model represents TD decision [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
read the original abstract

Technical Debt (TD) decisions are rarely just technical. Stakeholders consider heterogeneous criteria, such as deadlines, client expectations, architectural consequences, available resources, organizational authority, and team sustainability, when deciding whether to acquire, postpone, prioritize, or repay TD. However, these criteria are often reported under different labels and at different levels of abstraction, making it difficult to compare findings across TD decision contexts. This paper proposes a practitioner-informed taxonomy represented as a conceptual model of stakeholder criteria for TD decision-making. Based on a qualitative study with 11 software practitioners in Brazil, we extracted, consolidated, and organized criteria related to TD acquisition and repayment. The resulting taxonomy comprises six families: stakeholder-facing value; delivery and resource pressure; technical integrity and systemic risk; decision basis and epistemic style; governance and legitimation; and human and team sustainability. Our findings show that acquisition and repayment share taxonomic families but differ in decision function. In acquisition, criteria often operate as permission mechanisms that make shortcuts acceptable under current constraints. In repayment, criteria operate as authorization mechanisms that justify allocating time and resources to address existing debt. The conceptual model further shows how criteria become actionable through stakeholder interpretation, translation across technical and business perspectives, and organizational legitimation. The taxonomy and model provide an empirically grounded artifact for classifying TD decision criteria, comparing decision contexts, and structuring discussions about TD acquisition and repayment.

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 claims to propose a practitioner-informed taxonomy of stakeholder criteria for technical debt (TD) decision-making, derived from thematic analysis of interviews with 11 software practitioners in Brazil. The taxonomy organizes criteria into six families (stakeholder-facing value; delivery and resource pressure; technical integrity and systemic risk; decision basis and epistemic style; governance and legitimation; human and team sustainability) and distinguishes acquisition (criteria as permission mechanisms) from repayment (criteria as authorization mechanisms). A conceptual model illustrates how criteria become actionable via interpretation, translation, and legitimation, positioning the taxonomy as an artifact for classifying TD decision contexts across settings.

Significance. If the taxonomy holds, it supplies a useful empirically grounded vocabulary for comparing heterogeneous stakeholder considerations in TD decisions, which could help structure research and practice discussions in software engineering. The acquisition/repayment functional distinction and the emphasis on organizational legitimation add nuance beyond purely technical TD models.

major comments (2)
  1. [Qualitative study description] The qualitative study description (abstract and methods narrative): the consolidation step from raw criteria extracted from the 11 interviews to the six families is presented without an audit trail, inter-rater reliability statistics, or member-checking results. This directly affects the trustworthiness of the central taxonomy artifact.
  2. [Abstract and applicability claims] Abstract and discussion of applicability: the claim that the taxonomy 'supplies a stable artifact usable for classifying TD decision contexts across other settings' rests on transferability from a single-country sample of 11 practitioners, yet no evidence, sensitivity analysis, or discussion of potential missing families under different regulatory/market structures is provided. This assumption is load-bearing for the cross-context classification use case.
minor comments (1)
  1. [Abstract] The abstract could explicitly note the single-country limitation to set reader expectations for the taxonomy's scope.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We respond to each major comment below and note planned revisions.

read point-by-point responses

  1. Referee: The qualitative study description (abstract and methods narrative): the consolidation step from raw criteria extracted from the 11 interviews to the six families is presented without an audit trail, inter-rater reliability statistics, or member-checking results. This directly affects the trustworthiness of the central taxonomy artifact.

    Authors: We agree the methods narrative would benefit from greater transparency. In revision we will expand the methods section with a detailed audit trail, including examples of raw criteria extracted from interviews, initial codes, and the consolidation steps into the six families. The analysis was interpretive and led by one researcher with team discussion; formal inter-rater reliability statistics were not computed, which is standard for this style of thematic analysis. We will state this rationale explicitly. Member checking was not performed and will be noted as a limitation. revision: partial

  2. Referee: Abstract and discussion of applicability: the claim that the taxonomy 'supplies a stable artifact usable for classifying TD decision contexts across other settings' rests on transferability from a single-country sample of 11 practitioners, yet no evidence, sensitivity analysis, or discussion of potential missing families under different regulatory/market structures is provided. This assumption is load-bearing for the cross-context classification use case.

    Authors: We accept that the current wording overstates transferability. We will revise the abstract and discussion to present the taxonomy as a practitioner-informed artifact from the Brazilian sample rather than a stable cross-context classifier. A new limitations subsection will discuss the single-country, small-sample scope and logically consider how differing regulatory or market conditions could surface additional families, drawing on related TD literature. We will call for future validation studies. No sensitivity analysis is possible with the existing data. revision: yes

Circularity Check

0 steps flagged

No circularity: taxonomy derived from primary interview data

full rationale

The paper conducts thematic analysis on 11 new interviews to extract and consolidate criteria into six families. No equations, fitted parameters, predictions, or self-citations are used to derive the taxonomy; the central artifact is built directly from the collected practitioner accounts. The derivation chain is empirical and independent of prior author results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The taxonomy rests on the assumption that interview statements from 11 practitioners can be reliably extracted, consolidated, and organized into stable families without introducing researcher bias or requiring external validation data. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Qualitative interview data from a small purposive sample can be consolidated into a generalizable taxonomy of decision criteria.
    The paper performs extraction and consolidation from 11 interviews and presents the resulting families as usable for classification across contexts.

pith-pipeline@v0.9.1-grok · 5778 in / 1331 out tokens · 14436 ms · 2026-06-26T16:03:50.840418+00:00 · methodology

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Reference graph

Works this paper leans on

30 extracted references · 22 canonical work pages

  1. [1]

    Nicolli S. R. Alves, Leilane F. Ribeiro, Vivyane Caires, Thiago S. Mendes, and Rodrigo O. Spínola. 2014. Towards an Ontology of Terms on Technical Debt. In Proceedings of the 2014 Sixth International Workshop on Managing Technical Debt (MTD ’14). IEEE Computer Society, USA, 1–7. doi:10.1109/MTD.2014.9

    work page doi:10.1109/mtd.2014.9 2014

  2. [2]

    Anonymous. 2026. Anonymized for double-blind review. Anonymized journal (2026). Details omitted for double-blind review

    2026

  3. [3]

    Kenneth D. Bailey. 1994. Typologies and Taxonomies: An Introduction to Classi/f_ica- tion Techniques. Number 102 in Quantitative Applications in the Social Sciences. SAGE Publications, Thousand Oaks, CA. doi:10.4135/9781412986397 Stakeholder Criteria in Technical Debt Decision-Making SBES 2026, September 8–12, 2026, São Paulo, SP, Brazil

    work page doi:10.4135/9781412986397 1994

  4. [4]

    Christoph Becker, Fabian Fagerholm, Rahul Mohanani, and Alexander Chatzige- orgiou. 2019. Temporal discounting in technical debt: how do software practi- tioners discount the future?. In Proceedings of the Second International Conference on Technical Debt (Montreal, Quebec, Canada) (TechDebt ’19). IEEE Press, 23–32. doi:10.1109/TechDebt.2019.00011

    work page doi:10.1109/techdebt.2019.00011 2019

  5. [5]

    João Paulo Biazotto, Daniel Feitosa, Paris Avgeriou, and Elisa Yumi Nakagawa

  6. [6]

    Empirical Software Engineering 30, 5 (2025), 134

    Understanding practitioners’ reasoning and requirements for efficient tool support in technical debt management. Empirical Software Engineering 30, 5 (2025), 134. doi:10.1007/s10664-025-10691-5

    work page doi:10.1007/s10664-025-10691-5 2025

  7. [7]

    Ward Cunningham. 1992. The WyCash portfolio management system. In Adden- dum to the Proceedings on Object-Oriented Programming Systems, Languages, and Applications (Addendum) (Vancouver, British Columbia, Canada) (OOP- SLA ’92) . Association for Computing Machinery, New York, NY, USA, 29–30. doi:10.1145/157709.157715

    work page doi:10.1145/157709.157715 1992

  8. [8]

    Carlos Fernández-Sánchez, Juan Garbajosa, and Agustín Yagüe. 2015. A frame- work to aid in decision making for technical debt management. In 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD) . 69–76. doi:10.1109/ MTD.2015.7332628

    arXiv 2015

  9. [9]

    Carlos Fernández-Sánchez, Juan Garbajosa, Agustín Yagüe, and Jennifer Perez

  10. [10]

    Journal of Systems and Software 124 (2017), 22–38

    Identi/f_ication and analysis of the elements required to manage technical debt by means of a systematic mapping study. Journal of Systems and Software 124 (2017), 22–38. doi:10.1016/j.jss.2016.10.018

    work page doi:10.1016/j.jss.2016.10.018 2017

  11. [11]

    Sávio Freire, Nicolli Rios, Boris Gutierrez, Darío Torres, Manoel Mendonça, Clemente Izurieta, Carolyn Seaman, and Rodrigo O. Spínola. 2020. Surveying Software Practitioners on Technical Debt Payment Practices and Reasons for not Paying off Debt Items. In Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering ...

    work page doi:10.1145/3383219.3383241 2020

  12. [12]

    Sávio Freire, Nicolli Rios, Boris Pérez, Camilo Castellanos, Darío Correal, Robert Ramač, Vladimir Mandić, Nebojša Taušan, Alexia Pacheco, Gustavo López, Ma- noel Mendonça, Clemente Izurieta, Davide Falessi, Carolyn Seaman, and Rodrigo Spínola. 2021. Pitfalls and Solutions for Technical Debt Management in Agile Soft- ware Projects. IEEE Softw. 38, 6 (Nov....

    work page doi:10.1109/ms.2021.3101990 2021

  13. [13]

    Gomes, Diogo Alves de Moura Loiola, and Alcemir Ro- drigues Santos

    Tchalisson Brenne S. Gomes, Diogo Alves de Moura Loiola, and Alcemir Ro- drigues Santos. 2024. Technical Debt Tools: a Survey and an Empirical Evaluation. 12 (Aug. 2024), 8:1 – 8:16. doi:10.5753/jserd.2024.3591

    work page doi:10.5753/jserd.2024.3591 2024

  14. [14]

    Yuepu Guo, Rodrigo Oliveira Spínola, and Carolyn Seaman. 2016. Exploring the costs of technical debt management — a case study. Empirical Softw. Engg. 21, 1 (Feb. 2016), 159–182. doi:10.1007/s10664-014-9351-7

    work page doi:10.1007/s10664-014-9351-7 2016

  15. [15]

    Nord, and Ipek Ozkaya

    Philippe Kruchten, Robert L. Nord, and Ipek Ozkaya. 2012. Technical Debt: From Metaphor to Theory and Practice. IEEE Softw. 29, 6 (Nov. 2012), 18–21. doi:10.1109/MS.2012.167

    work page doi:10.1109/ms.2012.167 2012

  16. [16]

    Valentina Lenarduzzi, Terese Besker, Davide Taibi, Antonio Martini, and Francesca Arcelli Fontana. 2021. A systematic literature review on Technical Debt prioritization: Strategies, processes, factors, and tools. Journal of Systems and Software 171 (2021), 110827. doi:10.1016/j.jss.2020.110827

    work page doi:10.1016/j.jss.2020.110827 2021

  17. [17]

    Zengyang Li, Paris Avgeriou, and Peng Liang. 2015. A systematic mapping study on technical debt and its management. J. Syst. Softw. 101, C (March 2015), 193–220. doi:10.1016/j.jss.2014.12.027

    work page doi:10.1016/j.jss.2014.12.027 2015

  18. [18]

    Erin Lim, Nitin Taksande, and Carolyn Seaman. 2012. A Balancing Act: What Software Practitioners Have to Say about Technical Debt. IEEE Softw. 29, 6 (Nov. 2012), 22–27. doi:10.1109/MS.2012.130

    work page doi:10.1109/ms.2012.130 2012

  19. [19]

    Vladimir Mandić, Nebojša Taušan, Robert Ramač, Sávio Freire, Nicolli Rios, Boris Pérez, Camilo Castellanos, Darío Correal, Alexia Pacheco, Gustavo Lopez, Clemente Izurieta, Davide Falessi, Carolyn Seaman, and Rodrigo Spínola. 2021. Technical and Nontechnical Prioritization Schema for Technical Debt: Voice of TD-Experienced Practitioners. IEEE Softw. 38, 6...

    arXiv 2021

  20. [20]

    Antonio Martini, Jan Bosch, and Michel Chaudron. 2014. Architecture Technical Debt: Understanding Causes and a Qualitative Model. In Proceedings of the 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA ’14). IEEE Computer Society, USA, 85–92. doi:10.1109/SEAA.2014.65

    work page doi:10.1109/seaa.2014.65 2014

  21. [21]

    Antonio Martini, Jan Bosch, and Michel Chaudron. 2015. Investigating Architec- tural Technical Debt accumulation and refactoring over time. Inf. Softw. Technol. 67, C (Nov. 2015), 237–253. doi:10.1016/j.infsof.2015.07.005

    work page doi:10.1016/j.infsof.2015.07.005 2015

  22. [22]

    Nickerson, Upkar Varshney, and Jan Muntermann

    Robert C. Nickerson, Upkar Varshney, and Jan Muntermann. 2013. A Method for Taxonomy Development and Its Application in Information Systems. European Journal of Information Systems 22, 3 (2013), 336–359. doi:10.1057/ejis.2012.26

    work page doi:10.1057/ejis.2012.26 2013

  23. [23]

    Farias, Manoel Mendonça, and Ro- drigo Oliveira Spínola

    Leilane Ferreira Ribeiro, Mário André de F. Farias, Manoel Mendonça, and Ro- drigo Oliveira Spínola. 2016. Decision Criteria for the Payment of Technical Debt in Software Projects: A Systematic Mapping Study. In Proceedings of the 18th International Conference on Enterprise Information Systems (Rome, Italy) (ICEIS 2016). SCITEPRESS - Science and Technolog...

    work page doi:10.5220/0005914605720579 2016

  24. [24]

    Nicolli Rios, Manoel Gomes de Mendonça Neto, and Rodrigo Oliveira Spínola

  25. [25]

    Information and Software Technology 102 (2018), 117–145

    A tertiary study on technical debt: Types, management strategies, re- search trends, and base information for practitioners. Information and Software Technology 102 (2018), 117–145. doi:10.1016/j.infsof.2018.05.010

    work page doi:10.1016/j.infsof.2018.05.010 2018

  26. [26]

    Nicolli Rios, Rodrigo Oliveira Spínola, Manoel Mendonça, and Carolyn Seaman

  27. [27]

    In Proceedings of the 12th ACM/IEEE Inter- national Symposium on Empirical Software Engineering and Measurement (Oulu, Finland) (ESEM ’18)

    The most common causes and effects of technical debt: /f_irst results from a global family of industrial surveys. In Proceedings of the 12th ACM/IEEE Inter- national Symposium on Empirical Software Engineering and Measurement (Oulu, Finland) (ESEM ’18). Association for Computing Machinery, New York, NY, USA, Article 39, 10 pages. doi:10.1145/3239235.3268917

    work page doi:10.1145/3239235.3268917

  28. [28]

    Stochel, Tomasz Borek, Mariusz R

    Marek G. Stochel, Tomasz Borek, Mariusz R. Wawrowski, and Piotr Chołda. 2023. Business-driven technical debt management using Continuous Debt Valuation Approach (CoDV A).Inf. Softw. Technol. 164, C (Dec. 2023), 21 pages. doi:10.1016/ j.infsof.2023.107333

    arXiv 2023

  29. [29]

    Edith Tom, AybüKe Aurum, and Richard Vidgen. 2013. An exploration of technical debt. J. Syst. Softw. 86, 6 (June 2013), 1498–1516. doi:10.1016/j.jss.2012.12.052

    work page doi:10.1016/j.jss.2012.12.052 2013

  30. [30]

    Jesse Yli-Huumo, Andrey Maglyas, and Kari Smolander. 2016. How do software development teams manage technical debt? - An empirical study. J. Syst. Softw. 120, C (Oct. 2016), 195–218. doi:10.1016/j.jss.2016.05.018

    work page doi:10.1016/j.jss.2016.05.018 2016