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arxiv: 2506.23234 · v2 · submitted 2025-06-29 · 💻 cs.SE

From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers

Peerachai Banyongrakkul , Mansooreh Zahedi , Patanamon Thongtanunam , Christoph Treude , Haoyu Gao This is my paper

Pith reviewed 2026-05-19 07:55 UTC · model grok-4.3

classification 💻 cs.SE
keywords pre-trained modelsPTM reusesoftware engineeringgithub issueschallenge taxonomyissue resolutionopen source softwaremodel adoption
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The pith

Developers reusing pre-trained models encounter seven main challenge categories, with related issues taking longer to resolve than others.

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

This paper examines the specific obstacles that arise when downstream developers incorporate pre-trained AI models into existing software systems. The authors collected and coded 840 issue reports drawn from 31 open-source GitHub projects to build a taxonomy that groups the problems into seven categories such as model usage, performance, and output quality. They also compared resolution times and found that PTM-linked issues persist longer before closure, with measurable differences across the categories. A sympathetic reader cares because pre-trained models are now widely adopted yet their practical integration hurdles remain poorly catalogued, directly affecting development speed and maintenance effort in real projects.

Core claim

Through qualitative analysis of 840 PTM-related issue reports from 31 OSS GitHub projects, the authors develop a taxonomy of seven challenge categories that downstream developers face when reusing pre-trained models and show via statistical tests that these issues take significantly longer to resolve than non-PTM issues, with variation across categories.

What carries the argument

A taxonomy of seven PTM challenge categories built from qualitative coding of GitHub issue reports.

Load-bearing premise

The 31 selected open-source projects and their 840 issue reports represent the broader population of pre-trained model reuse scenarios and the qualitative coding process captured all relevant challenge types without systematic bias.

What would settle it

A larger study sampling hundreds of additional projects and re-running the resolution-time comparison finds either a substantially different set of challenge categories or no statistically significant difference in time-to-resolution between PTM and non-PTM issues.

Figures

Figures reproduced from arXiv: 2506.23234 by Christoph Treude, Haoyu Gao, Mansooreh Zahedi, Patanamon Thongtanunam, Peerachai Banyongrakkul.

Figure 1
Figure 1. Figure 1: A motivating example illustrating a PTM-related chal [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: An example of the data analysis process for developing the taxonomy of PTM-related challenges. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The top two levels of our taxonomy of PTM-related challenges in downstream software projects, where each challenge [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Resolution time analysis of PTM-related issues. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Pre-trained models (PTMs) have gained widespread popularity and achieved remarkable success across various fields, driven by their groundbreaking performance and easy accessibility through hosting providers. However, the challenges faced by downstream developers in reusing PTMs in software systems are less explored. To bridge this knowledge gap, we qualitatively created and analyzed a dataset of 840 PTM-related issue reports from 31 OSS GitHub projects. We systematically developed a comprehensive taxonomy of PTM-related challenges that developers face in downstream projects. Our study identifies seven key categories of challenges that downstream developers face in reusing PTMs, such as model usage, model performance, and output quality. We also compared our findings with existing taxonomies. Additionally, we conducted a resolution time analysis and, based on statistical tests, found that PTM-related issues take significantly longer to be resolved than issues unrelated to PTMs, with significant variation across challenge categories. We discuss the implications of our findings for practitioners and possibilities for future research.

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 reports a qualitative study analyzing 840 PTM-related GitHub issues from 31 open-source projects to develop a taxonomy of seven challenge categories for downstream developers reusing pre-trained AI models. It also includes a statistical analysis comparing resolution times of PTM-related versus unrelated issues.

Significance. If the sampling and coding procedures are robust, the work offers a valuable empirical taxonomy of real-world challenges in PTM adoption, extending prior taxonomies and highlighting practical implications for longer resolution times. The use of a large dataset of actual developer issues and statistical tests strengthens the contribution to software engineering research on AI model reuse.

major comments (2)
  1. The selection criteria for the 31 OSS GitHub projects and the process for identifying and extracting the 840 PTM-related issue reports (including inclusion/exclusion rules and sampling frame) are not sufficiently detailed. This is load-bearing for claims of representativeness in the seven-category taxonomy and the resolution-time statistical comparisons.
  2. The qualitative coding process that produced the seven challenge categories lacks any reporting of inter-rater agreement or reliability metrics (e.g., Cohen's kappa or percentage agreement). This directly affects the validity of the taxonomy and the subsequent category-specific resolution time analysis.
minor comments (2)
  1. The abstract states that PTM-related issues take significantly longer to resolve but does not name the specific statistical test(s) used; add this detail for precision.
  2. A table comparing the new seven-category taxonomy with prior work (mentioned in the abstract) would improve clarity and allow readers to see overlaps and gaps at a glance.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of methodological transparency that we will address in the revision to strengthen the presentation of our qualitative taxonomy and statistical findings. We respond to each major comment below.

read point-by-point responses

  1. Referee: The selection criteria for the 31 OSS GitHub projects and the process for identifying and extracting the 840 PTM-related issue reports (including inclusion/exclusion rules and sampling frame) are not sufficiently detailed. This is load-bearing for claims of representativeness in the seven-category taxonomy and the resolution-time statistical comparisons.

    Authors: We agree that the current description of project selection and issue extraction could be expanded for greater clarity and to better support claims about the taxonomy and statistical comparisons. In the revised manuscript, we will add a dedicated subsection under Methodology that details the criteria for selecting the 31 projects (e.g., minimum stars, activity level, and evidence of PTM usage), the overall sampling frame from GitHub, and the precise inclusion/exclusion rules applied to filter the 840 PTM-related issues from the initial set of reports. revision: yes

  2. Referee: The qualitative coding process that produced the seven challenge categories lacks any reporting of inter-rater agreement or reliability metrics (e.g., Cohen's kappa or percentage agreement). This directly affects the validity of the taxonomy and the subsequent category-specific resolution time analysis.

    Authors: We acknowledge that explicit reporting of inter-rater reliability would improve the perceived rigor of the taxonomy development. The coding was performed iteratively by two authors with regular consensus discussions to resolve disagreements. In the revision, we will describe this process in more detail within the Data Analysis section and report agreement metrics (including Cohen's kappa calculated on a randomly sampled subset of issues) to address concerns about validity. revision: yes

Circularity Check

0 steps flagged

Empirical study reports observations from external GitHub data with no derivation chain

full rationale

This is a qualitative empirical study that extracts 840 PTM-related issue reports from 31 external OSS GitHub projects, performs qualitative coding to produce a seven-category taxonomy, compares the taxonomy to prior work, and runs statistical tests on resolution times. No equations, fitted parameters, predictions, or self-referential derivations are present. All claims rest on direct analysis of independently sourced external data rather than reducing to the paper's own inputs by construction. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study relies on standard qualitative research practices in empirical software engineering without introducing new mathematical parameters or postulated entities.

axioms (1)
  • domain assumption Qualitative coding of issue reports can produce a reliable and comprehensive taxonomy of developer challenges
    Core premise of the taxonomy construction step described in the abstract.

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