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arxiv: 2606.00118 · v1 · pith:VV6HJQRJnew · submitted 2026-05-27 · 💻 cs.SE · cs.PL

An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges

Patrick Loic Foalem , Andre Nguimbous , Foutse Khomh , Heng Li , Ettore Merlo This is my paper

Pith reviewed 2026-06-29 10:30 UTC · model grok-4.3

classification 💻 cs.SE cs.PL
keywords loggingstack overflowempirical studytopic classificationcontainerized environmentssoftware engineering challengesQ&A analysisdebugging
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The pith

Logging in containerized environments is the most challenging topic on Stack Overflow, with 64.9% of questions lacking accepted answers.

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

This paper examines 216,094 Stack Overflow posts on logging to map practitioner discussions and difficulties from a real-world perspective. It applies an LLM-based method, checked against a manual ground-truth sample, to sort posts into 11 topics and then ranks those topics by popularity and by three community metrics of difficulty. The top three topics cover more than 70% of the posts, yet the analysis singles out logging in containerized environments as clearly the hardest, with the highest share of questions without accepted answers and among the longest median resolution times. These patterns point to persistent integration problems when developers try to combine logging pipelines with Docker, Kubernetes, and cloud setups.

Core claim

Our analysis identifies 11 distinct topics, with the top three (General Logging Practices, Error Handling and Debugging, and Logging Levels and Output) accounting for over 70% of all logging-related discussions. Notably, Logging in Containerized Environments emerged as the most difficult topic: 64.9% of its questions lack an accepted answer, and its median resolution time is among the highest. These findings highlight enduring practitioner struggles with logging in Docker or other containerized environments and the integration of logging pipelines into orchestrators such as Kubernetes and cloud environments.

What carries the argument

LLM-based classification of posts into 11 topics validated on a manually checked ground-truth sample, together with three community metrics of difficulty: share of questions without accepted answers, share of unanswered questions, and median time to an accepted answer.

If this is right

  • General Logging Practices, Error Handling and Debugging, and Logging Levels and Output together dominate more than 70% of logging questions.
  • Logging in Containerized Environments shows the worst difficulty scores of the 11 topics.
  • The data indicate concrete integration problems when logging pipelines meet Docker, Kubernetes, and cloud orchestrators.
  • The results supply direct guidance for developers, framework vendors, researchers, and educators on where logging support is weakest.

Where Pith is reading between the lines

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

  • Tool vendors could reduce unanswered questions by shipping clearer logging examples and APIs for container orchestration platforms.
  • Educators might add targeted modules on container logging to lower the observed resolution times.
  • Repeating the same classification on GitHub issues or other forums could test whether the difficulty ranking holds outside Stack Overflow.

Load-bearing premise

The LLM classifier assigns posts to the 11 topics correctly and the three chosen community metrics accurately measure how difficult each topic is.

What would settle it

A fresh manual review of several thousand posts that produces a materially different topic distribution or changes which topic ranks as most difficult on the three metrics would falsify the central claims.

Figures

Figures reproduced from arXiv: 2606.00118 by Andre Nguimbous, Ettore Merlo, Foutse Khomh, Heng Li, Patrick Loic Foalem.

Figure 1
Figure 1. Figure 1: Overview of our methodology 2 Experiment setup To explore logging practices and their evolution on Stack Overflow, we adopted a mixed-methods approach combining qualitative and quantitative analyses similar to methodologies used in prior studies (Gujral et al., 2018; Openja et al., 2020; Ouni et al., 2023; Wen et al., 2021; Yahmed et al., 2023). This approach focuses on collecting and analyzing posts from … view at source ↗
Figure 2
Figure 2. Figure 2: Popularity trend of logging post on SO over years. [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Logging tags popularity growth over years. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of concepts associated with logging. [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Logging topics by the percentage of their corresponding questions. [PITH_FULL_IMAGE:figures/full_fig_p024_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of survey responses on the industrial relevance of the logging [PITH_FULL_IMAGE:figures/full_fig_p026_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of logging topics by popularity & difficulty [PITH_FULL_IMAGE:figures/full_fig_p030_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of survey responses on the perceived difficulty of logging topics. [PITH_FULL_IMAGE:figures/full_fig_p031_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of survey responses comparing the engineering effort required [PITH_FULL_IMAGE:figures/full_fig_p032_9.png] view at source ↗
Figure 4
Figure 4. Figure 4: However, these critical areas receive limited attention from academia, [PITH_FULL_IMAGE:figures/full_fig_p033_4.png] view at source ↗
read the original abstract

Context: Logging is a crucial practice in software engineering, aiding developers in debugging applications when errors occur. While existing research has explored logging challenges from an academic perspective through literature reviews and source code analysis, a comprehensive study from the practitioners' perspective remains lacking. Objective: This paper aims to bridge this knowledge gap by presenting an in-depth analysis of trends, topics, and challenges in logging based on a dataset of 216,094 posts from Stack Overflow (SO), a popular Q\&A platform for developers. Method: We analyzed longitudinal trends by examining metadata related to users, questions, and tags associated with logging discussions. To identify prevalent discussion topics, we employed a Large Language Model (LLM)--based classification approach, based on a manually validated ground-truth sample. Topic popularity was assessed through average scores and views, while difficulty was measured using three community-driven metrics: the proportion of questions without accepted answers, the proportion of unanswered questions, and the median time to receive an accepted answer. Results: Our analysis identifies 11 distinct topics, with the top three (General Logging Practices, Error Handling and Debugging, and Logging Levels and Output) accounting for over 70\% of all logging-related discussions. Notably, Logging in Containerized Environments emerged as the most difficult topic: 64.9\% of its questions lack an accepted answer, and its median resolution time is among the highest. These findings highlight enduring practitioner struggles with logging in Docker or other containerized environments and the integration of logging pipelines into orchestrators such as Kubernetes and cloud environments. Conclusion: This study sheds light on the practical challenges of logging and provides actionable insights for developers, framework vendors, researchers, and educators.

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

1 major / 2 minor

Summary. The paper reports an empirical analysis of 216,094 Stack Overflow posts on logging. It examines longitudinal trends through user/question/tag metadata, applies an LLM-based classifier (validated against a manually checked ground-truth sample) to derive 11 topics, measures popularity via average scores and views, and quantifies difficulty via three community metrics (fraction without accepted answers, fraction unanswered, and median time to accepted answer). Key results are that the top three topics (General Logging Practices, Error Handling and Debugging, Logging Levels and Output) cover >70% of posts and that Logging in Containerized Environments is the hardest topic (64.9% lack an accepted answer, high median resolution time).

Significance. If the classification and metric results hold, the study supplies a large-scale, practitioner-grounded complement to existing academic logging literature. The scale of the dataset and the use of observable community metrics (accepted-answer rate, resolution time) are strengths that could support reproducible follow-up work. The identification of containerized environments as a persistent difficulty area offers concrete, actionable signals for tool builders, educators, and framework maintainers.

major comments (1)
  1. [Method] Method section (LLM classification paragraph): the claim that classification rests on 'a manually validated ground-truth sample' supplies no sample size, sampling procedure, inter-annotator agreement statistic, or per-topic confusion matrix. Because the headline topic distribution (>70% in top three) and the difficulty ranking of 'Logging in Containerized Environments' (64.9% no accepted answer) are direct functions of the 11-way partition of the 216 k posts, the absence of these validation quantities is load-bearing for the central empirical claims.
minor comments (2)
  1. [Results] Results section: the three difficulty metrics are presented without any control for potential confounders (e.g., question quality, asker reputation, tag visibility) that could correlate with topic; a short discussion of this would strengthen interpretability.
  2. [Abstract] Abstract and title: 'Evolution' appears in the title yet the reported analysis is cross-sectional topic classification plus aggregate trends; clarifying whether temporal evolution of the 11 topics themselves was measured would remove ambiguity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the thorough review and the recommendation for major revision. We have carefully considered the referee's comment on the validation of our LLM-based topic classification and provide a point-by-point response below. We commit to revising the manuscript accordingly to enhance the transparency and reproducibility of our methods.

read point-by-point responses

  1. Referee: [Method] Method section (LLM classification paragraph): the claim that classification rests on 'a manually validated ground-truth sample' supplies no sample size, sampling procedure, inter-annotator agreement statistic, or per-topic confusion matrix. Because the headline topic distribution (>70% in top three) and the difficulty ranking of 'Logging in Containerized Environments' (64.9% no accepted answer) are direct functions of the 11-way partition of the 216 k posts, the absence of these validation quantities is load-bearing for the central empirical claims.

    Authors: We agree that the current manuscript does not provide sufficient details on the ground-truth sample used to validate the LLM classifier. This information is indeed critical for assessing the reliability of the 11-topic classification and the subsequent analyses. In the revised version, we will expand the Method section to report the sample size, the sampling procedure employed, the inter-annotator agreement statistics, and include a per-topic confusion matrix. These additions will directly address the load-bearing nature of the validation for our empirical claims. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical analysis of external public data

full rationale

The paper performs a standard empirical study on 216k Stack Overflow posts, using LLM classification validated against a manually checked ground-truth sample and three community metrics computed directly from post metadata. No equations, fitted parameters, predictions derived from subsets of the same data, or self-citation chains appear in the derivation. All reported statistics (topic distributions, difficulty rankings) are direct aggregates of observed SO data and are therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard assumptions in empirical software engineering research about the representativeness of SO data and the validity of LLM classification after manual validation; no free parameters or invented entities are introduced beyond the choice of 11 topics and the three difficulty metrics.

axioms (1)
  • domain assumption The manually validated ground-truth sample is representative and sufficient to confirm LLM classification accuracy across the full 216k posts.
    Invoked to support the topic identification results in the method and results sections of the abstract.

pith-pipeline@v0.9.1-grok · 5859 in / 1190 out tokens · 32690 ms · 2026-06-29T10:30:12.577608+00:00 · methodology

discussion (0)

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