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arxiv: 2605.16133 · v1 · pith:FCCKJDHAnew · submitted 2026-05-15 · 💻 cs.SE

The Dangers of Non-Self-Fixed Architecture Technical Debt and Its Impact on Time-to-Fix

Edi Sutoyo , Paris Avgeriou , Andrea Capiluppi This is my paper

Pith reviewed 2026-05-20 16:20 UTC · model grok-4.3

classification 💻 cs.SE
keywords architectural technical debtself-fixed debtnon-self-fixed debttime-to-fixopen source projectsempirical studycollaboration metricssurvival analysis
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The pith

Non-self-fixed architectural technical debt takes longer to resolve when changes involve many developers.

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

The paper compares self-fixed architectural technical debt, repaid by the developer who introduced it, with non-self-fixed debt repaid by others in large Apache open-source projects. It reconstructs debt lifecycles by connecting issue trackers to code history to measure introduction and repayment points. The results indicate that non-self-fixed items persist longer overall and especially when the affected files receive changes from many different developers. This pattern highlights how developer handoffs can extend the lifespan of structural problems in software.

Core claim

Self-fixed and non-self-fixed ATD exhibit distinct repayment dynamics and differences in how changes are shared on ATD-affected files. In particular, non-self-fixed ATD is more likely to remain unresolved longer when changes are spread across many developers.

What carries the argument

Reconstruction of ATD lifecycles through tracing of Jira artifacts to version-control history, used to identify introduction and repayment points and to classify items as self-fixed or non-self-fixed.

If this is right

  • Maintainers can reduce time-to-fix by encouraging the original introducer to handle repayment of ATD when possible.
  • ATD items on files changed by many developers warrant closer monitoring because they show slower resolution.
  • Documenting design rationale at the time of repayment lowers future handoff costs for non-self-fixed cases.
  • Project practices that minimize unnecessary developer turnover on ATD-affected code can shorten repayment times.

Where Pith is reading between the lines

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

  • Similar patterns may appear in closed-source teams where knowledge transfer between developers is limited.
  • Tools that track ATD could add alerts when an item shifts from self-fixed to non-self-fixed status.
  • Training new contributors on existing architectural decisions might reduce the number of non-self-fixed cases.

Load-bearing premise

Linking Jira artifacts to version-control history accurately attributes which developer introduced each ATD item and which one repaid it, and correctly labels the cases as self-fixed or non-self-fixed.

What would settle it

A manual audit of a random sample of classified ATD items that finds substantial mismatches in developer attribution or in the recorded dates of introduction and repayment.

Figures

Figures reproduced from arXiv: 2605.16133 by Andrea Capiluppi, Edi Sutoyo, Paris Avgeriou.

Figure 1
Figure 1. Figure 1: Overview of the lifecycle of ATD items. Operational definitions of the key moments. The Introduction and Repayment moments are observable in practice as Git commits. An intro￾duction commit may add a workaround, hard-coded configuration, shortcut, or dependency structure that violates intended modular boundaries. A re￾payment commit removes or mitigates the shortcut by restoring architectural boundaries, i… view at source ↗
Figure 2
Figure 2. Figure 2: An overview of the data collection. 1. Dataset filtering. We remove invalid entries (e.g., duplicates) and issues whose resolution indicates no associated code changes (when available). 2. Tracing payment commits. Since commit hashes are not directly stored in the dataset, we scan each repository’s Git history and extract com- [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overall distribution of self-fixed and non–self-fixed ATD items across [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of self-fixed and non–self-fixed ATD items grouped by [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Boxplot ratio of commits per file by Self-fixer and Others for self-fixed [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Boxplot ratio of commits per file by Introducer, Fixer, and Others for [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Kaplan–Meier cumulative fixed curves comparing self-fixed and non– [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Kaplan–Meier cumulative fixed curves for self-fixed and non–self-fixed [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Kaplan-Meier cumulative fixed curves for VioMod-related ATD items [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Kaplan-Meier cumulative fixed curves for ObsTech-related ATD items [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Early 30 days Kaplan-Meier cumulative fixed for VioMod. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Early 30 days Kaplan-Meier cumulative fixed for ObsTech. [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Boxplot of IIR [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗
Figure 16
Figure 16. Figure 16: Kaplan–Meier curves fixed by IIR for non–self-fixed ATD items. [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Kaplan–Meier curves fixed by FIR for non–self-fixed ATD items. [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Kaplan–Meier curves fixed by OIR for non–self-fixed ATD items. [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Distribution of code change and development activity metrics for self [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Developer seniority (in years since first project commit) for self-fixed [PITH_FULL_IMAGE:figures/full_fig_p028_20.png] view at source ↗
read the original abstract

Technical Debt (TD) refers to the long-term costs incurred when developers prioritize short-term delivery over quality-improving work. Architectural Technical Debt (ATD) arises when architectural decisions (e.g., technology choices, patterns, or decomposition) prioritize near-term progress over future maintainability and evolvability. Because ATD affects a system's core structure and propagates through architectural dependencies, it is often more expensive and disruptive to remediate than localized code-level debt. Although ATD has been widely studied, an important but underexplored aspect of repayment is who performs it. Prior work provides limited empirical evidence on repayment responsibility in ATD and its relationship to time-to-fix. We empirically study self-fixed ATD, where the introducer also repays the debt, and contrast it with non-self-fixed ATD in large Apache open-source projects. We reconstruct ATD lifecycles by tracing Jira artifacts to version-control history to identify introduction and repayment points and attribute developer roles. We address three research questions on the prevalence of self-fixed ATD, time-to-fix differences between self-fixed and non--self-fixed items, and how factors related to code change and collaboration metrics relate to repayment speed. Using descriptive statistics, non-parametric tests, and survival analysis, we show that self-fixed and non--self-fixed ATD exhibit distinct repayment dynamics and differences in how changes are shared on ATD-affected files. In particular, non--self-fixed ATD is more likely to remain unresolved longer when changes are spread across many developers. These results provide actionable guidance for maintainers to identify high-risk ATD items and to reduce handoff costs by increasing introducer involvement when possible and documenting the design rationale during 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 / 2 minor

Summary. The manuscript empirically examines self-fixed versus non-self-fixed architectural technical debt (ATD) in large Apache open-source projects. It reconstructs ATD lifecycles by tracing Jira artifacts to version-control history to identify introduction and repayment points and attribute developer roles. Using descriptive statistics, non-parametric tests, and survival analysis, the authors investigate prevalence, time-to-fix differences, and how code-change spread and collaboration metrics relate to repayment speed. The central claim is that non-self-fixed ATD remains unresolved longer when changes are spread across many developers.

Significance. If the ATD identification and developer-role attribution hold, the work offers actionable guidance for maintainers on reducing handoff costs in ATD repayment and highlights the interaction between collaboration patterns and debt persistence. The application of survival analysis to model time-to-fix is a strength, as is the focus on the 'who' dimension of repayment, which has received limited prior attention. The results could inform project practices if the reconstruction method is shown to be robust.

major comments (2)
  1. [Methodology / Data Reconstruction] The reconstruction of ATD lifecycles relies on linking Jira artifacts to VCS history for introduction/repayment points and self/non-self classification, yet the manuscript provides no detail on ATD identification rules, inter-rater reliability for classification, or explicit handling of confounding factors such as incomplete commit references or rebases. This is load-bearing for the survival-analysis results and the claim about developer-spread effects.
  2. [Section 4 (Lifecycle Reconstruction)] The skeptic concern about Jira-to-VCS linking accuracy directly affects the weakest assumption: if multi-contributor commits or timestamp misalignments lead to noisy self-fixed/non-self-fixed labels, both the grouping variable and the observed correlations with change-spread metrics become unreliable, undermining the time-to-fix comparisons.
minor comments (2)
  1. [Section 3.1] Clarify the exact operational definition of 'architectural' debt versus other TD types when selecting Jira issues; this would help readers assess generalizability.
  2. [Abstract and Results] The abstract states that non-self-fixed ATD is 'more likely to remain unresolved longer' under high developer spread; add a specific hazard-ratio or median time-to-fix value from the survival model to make this quantitative.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and detailed review of our manuscript. The comments highlight important aspects of our methodology that require greater transparency to support the validity of our empirical findings. We address each major comment below and indicate the specific revisions we will incorporate in the next version of the paper.

read point-by-point responses

  1. Referee: [Methodology / Data Reconstruction] The reconstruction of ATD lifecycles relies on linking Jira artifacts to VCS history for introduction/repayment points and self/non-self classification, yet the manuscript provides no detail on ATD identification rules, inter-rater reliability for classification, or explicit handling of confounding factors such as incomplete commit references or rebases. This is load-bearing for the survival-analysis results and the claim about developer-spread effects.

    Authors: We agree that additional methodological detail is needed to allow readers to fully assess the reconstruction process and its potential impact on the survival analysis. In the revised manuscript we will expand Section 4 with a dedicated subsection that (1) enumerates the precise ATD identification rules applied to Jira issues, (2) reports any inter-rater reliability statistics obtained during classification, and (3) describes our explicit procedures for mitigating confounding factors such as incomplete commit references and rebases. These additions will directly strengthen the grounding of the time-to-fix comparisons and the developer-spread findings. revision: yes

  2. Referee: [Section 4 (Lifecycle Reconstruction)] The skeptic concern about Jira-to-VCS linking accuracy directly affects the weakest assumption: if multi-contributor commits or timestamp misalignments lead to noisy self-fixed/non-self-fixed labels, both the grouping variable and the observed correlations with change-spread metrics become unreliable, undermining the time-to-fix comparisons.

    Authors: We recognize that linking inaccuracies could introduce noise into the self-fixed versus non-self-fixed labels. In the revision we will add a detailed account of the Jira-to-VCS linking algorithm, including how multi-contributor commits and timestamp discrepancies were resolved. We will also report a sensitivity analysis that quantifies the effect of plausible misclassification rates on the survival curves and on the change-spread correlations. This analysis will demonstrate that the core conclusions remain stable under reasonable levels of label noise. revision: yes

Circularity Check

0 steps flagged

Empirical reconstruction from external artifacts with standard statistics shows no circularity

full rationale

The paper conducts an empirical study that reconstructs ATD introduction and repayment points by linking Jira artifacts to version-control history in Apache projects, then applies descriptive statistics, non-parametric tests, and survival analysis to compare self-fixed versus non-self-fixed items and their relation to developer spread. No equations, fitted parameters, or predictions are defined in terms of the target outcomes; the self/non-self classification follows directly from observable commit authorship matches rather than any self-referential construction. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work appear in the provided derivation chain. The central claim on time-to-fix differences therefore rests on independent external data traces and conventional statistical methods, rendering the analysis self-contained against the project artifacts.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the fidelity of lifecycle reconstruction from issue trackers and version control; no free parameters or invented entities are introduced, but domain assumptions about data traceability are load-bearing.

axioms (1)
  • domain assumption Jira issues and linked commits can be used to reliably identify architectural technical debt introduction and repayment events.
    The reconstruction of ATD lifecycles rests on this traceability assumption stated in the abstract.

pith-pipeline@v0.9.0 · 5852 in / 1231 out tokens · 83543 ms · 2026-05-20T16:20:20.937268+00:00 · methodology

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