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arxiv: 2606.04716 · v1 · pith:YQW625HPnew · submitted 2026-06-03 · 💻 cs.SE

The State of Peer Review in Empirical Software Engineering: A Community Survey on Review Load, Quality, and GenAI Use

Justus Bogner , Roberto Verdecchia This is my paper

Pith reviewed 2026-06-28 05:27 UTC · model grok-4.3

classification 💻 cs.SE
keywords peer reviewempirical software engineeringcommunity surveyreview loadreview qualitygenerative AILLM toolsquestionnaire
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The pith

A survey of 120 empirical software engineering researchers documents current review loads, quality problems, LLM tool use, and improvement ideas.

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

The paper reports results from a questionnaire survey that collected 120 responses from members of the empirical software engineering community. It presents data on perceived review workload, views on review quality and common problems, the adoption of LLM-based tools during reviewing, and participant suggestions for changes to the system. The authors intend these findings to support more evidence-based conversations about reforming peer review in the face of rising submissions and generative AI.

Core claim

The survey of 120 ESE community members documents perceived review load, quality issues, frequent challenges, LLM-based tool use in reviewing, and community suggestions for improving the peer review system.

What carries the argument

The questionnaire survey with 120 self-selected responses that gathers community perceptions on review load, quality, GenAI use, and system improvements.

If this is right

  • Community members experience notable review load that contributes to system strain.
  • Review quality is perceived to face recurring challenges and issues.
  • LLM-based tools have entered the reviewing workflow with associated concerns.
  • The community holds concrete ideas for targeted improvements to peer review processes.

Where Pith is reading between the lines

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

  • If these perceptions hold more widely, conferences and journals may need to adjust reviewer assignment policies or introduce workload caps.
  • Similar surveys in adjacent fields such as computer science theory or human-computer interaction could test whether the reported patterns are domain-specific.
  • Explicit guidelines on acceptable LLM assistance during review could become a standard requirement if tool use continues to grow.

Load-bearing premise

The 120 self-selected respondents provide a sufficiently representative picture of the broader empirical software engineering community to support general statements about review load and quality.

What would settle it

A follow-up survey using random sampling or a much larger response pool that finds substantially different average perceptions of review load or quality would undermine the generalizability of these results.

Figures

Figures reproduced from arXiv: 2606.04716 by Justus Bogner, Roberto Verdecchia.

Figure 1
Figure 1. Figure 1: Geographical distribution of the 120 participants [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of ESE reviewing effort across workshops, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Perceived review quality in the last year [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Perceived best qualities of provided reviews [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Most frequent reasons to argue for rejecting papers (coded “other:” options marked with an asterisk) [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Most frequent issues noted in other reviews for the same [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Self-reported LLM use during peer review (coded [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Opinions on LLM use in ESE peer review Review load & incentives (42) Governance & repercussions (29) LLM use in review (24) Cultural changes (17) Reviewer training (17) Process & collaboration (14) Improve reviewing incentives (13) Apply early desk-rejection (11) Introduce review token model (5) Reduce review workload (5) Reduce # of reviewers per paper (3) Increase PC members / review time (5) Check and … view at source ↗
Figure 11
Figure 11. Figure 11: Coded open-ended suggestions on how to improve ESE [PITH_FULL_IMAGE:figures/full_fig_p007_11.png] view at source ↗
read the original abstract

The scientific peer review system has been slowly deteriorating over the last years, and not just within empirical software engineering (ESE) research. Increased submission numbers, high workload, and the rise of generative AI use with all its associated issues have made many cracks in the system more visible. To get a better understanding of the current state of peer review in the ESE community, we conducted a questionnaire survey, which accumulated 120 responses. We report on (i) the perceived review load of community members, (ii) review quality perception as well as frequent challenges for and issues with reviews, (iii) the use of LLM-based tools in the reviewing process, and (iv) the community's suggestions for improving the peer review system. We hope that these community opinions can facilitate more evidence-based discussions about how people want to see the review system change for the better.

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 presents results from an online questionnaire survey that received 120 responses from members of the empirical software engineering (ESE) community. It reports descriptive statistics and qualitative themes on (i) perceived review load, (ii) perceptions of review quality together with common challenges and issues, (iii) use of LLM-based tools during reviewing, and (iv) community suggestions for improving the peer-review system.

Significance. If the sample were demonstrably representative, the work would supply useful community-sourced data on review workload, quality problems, and emerging GenAI practices that could inform evidence-based discussions about peer-review reform in ESE. The paper's strength lies in its direct, unmodeled reporting of respondent answers against external benchmarks; no fitted parameters or invented constructs are introduced.

major comments (1)
  1. [§3 and Abstract] §3 (Survey Design and Administration) and Abstract: The central claim that the survey documents 'the state of peer review in the ESE community' rests on the assumption that the 120 self-selected respondents are sufficiently representative. The manuscript provides no information on distribution channels, total invitations sent, response rate, handling of non-response bias, or demographic benchmarking of respondents against the ESE population (e.g., via DBLP or conference attendance data). Because every reported percentage, theme, and suggestion depends on this assumption, the absence of these details is load-bearing for the paper's primary contribution.
minor comments (2)
  1. [Demographics table] Table 1 (or equivalent respondent demographics table): Clarify whether the reported percentages are of all 120 respondents or of the subset who answered each question; missing-data handling should be stated explicitly.
  2. [§4.3] §4.3 (LLM use): The distinction between 'using LLMs to draft reviews' and 'using LLMs to check grammar' is important for policy implications; ensure the questionnaire items and response categories make this distinction unambiguous.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our survey paper. We agree that the sampling approach and its implications require clearer exposition and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§3 and Abstract] §3 (Survey Design and Administration) and Abstract: The central claim that the survey documents 'the state of peer review in the ESE community' rests on the assumption that the 120 self-selected respondents are sufficiently representative. The manuscript provides no information on distribution channels, total invitations sent, response rate, handling of non-response bias, or demographic benchmarking of respondents against the ESE population (e.g., via DBLP or conference attendance data). Because every reported percentage, theme, and suggestion depends on this assumption, the absence of these details is load-bearing for the paper's primary contribution.

    Authors: We agree that the manuscript should provide more information on the survey administration and explicitly address potential biases. We will revise §3 to include all available details on how the survey was distributed and add a limitations section discussing the self-selected sample, absence of response rate information, and lack of formal benchmarking against the broader ESE population. We will also update the abstract to more accurately reflect that the results capture perceptions from a self-selected group of community members. These changes will strengthen the paper by clarifying the scope of the claims. revision: yes

Circularity Check

0 steps flagged

No circularity: direct survey reporting with no derivations or self-referential steps

full rationale

This is a questionnaire survey paper reporting aggregated responses from 120 participants on review load, quality, LLM use, and improvement suggestions. The provided abstract and description contain no equations, model derivations, fitted parameters, predictions, or load-bearing self-citations. All content is a direct summary of collected data, with no reduction of claims to inputs by construction. The representativeness of the sample is an external validity concern, not a circularity issue in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that voluntary survey responses can be treated as informative about the community at large; no free parameters, invented entities, or mathematical axioms are involved.

axioms (1)
  • domain assumption The 120 self-selected survey responses are representative enough of the ESE community to support statements about review load and quality.
    Survey-based claims about community state require this premise for external validity.

pith-pipeline@v0.9.1-grok · 5682 in / 1207 out tokens · 20541 ms · 2026-06-28T05:27:34.362268+00:00 · methodology

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