[Paper] Towards A Sustainable Future for Peer Review in Software Engineering

Source: arXiv - 2601.21761v1

Overview

The paper Towards A Sustainable Future for Peer Review in Software Engineering examines the mounting strain on the peer‑review ecosystem that underpins software‑engineering (SE) research. By diagnosing why reviewer shortages are becoming a bottleneck, the authors sketch a roadmap for a more scalable, inclusive, and AI‑augmented review process that can keep pace with the field’s rapid growth.

Key Contributions

• Empirical diagnosis of reviewer supply‑demand imbalance across major SE venues (ICSE, FSE, ASE, etc.).
• Three‑pronged vision for a sustainable review pipeline:
  1. Systematic onboarding & training of new reviewers
  2. Incentive structures that broaden participation
  3. Cautious integration of AI‑assistance
• Prototype reviewer‑training curriculum (online modules, mentorship pairings, and micro‑review tasks) evaluated in a pilot with 48 early‑career researchers.
• Incentive framework that combines reputation‑based badges, reviewer‑credit tokens, and conference‑submission discounts.
• Proof‑of‑concept AI toolchain (paper‑summarization, plagiarism detection, and checklist compliance) tested on a sample of 200 submissions, measuring time‑saved and error‑rate impact.
• Open‑source repository of datasets, guidelines, and tooling to enable community adoption and further research.

Methodology

1. Data Collection & Analysis – Harvested submission and reviewer assignment logs from the past five years of top SE conferences, quantifying reviewer load, turnaround times, and acceptance ratios.
2. Survey & Interviews – 312 SE researchers (students, faculty, industry practitioners) answered a questionnaire about reviewing experiences, motivations, and pain points; 27 follow‑up semi‑structured interviews deepened the insights.
3. Design of Interventions – Co‑designed three interventions (training, incentives, AI support) using participatory design workshops with conference organizers and senior reviewers.
4. Pilot Evaluation – Conducted a controlled pilot during the 2024 SE conference season:
   • 48 novice reviewers followed the training curriculum
   • 120 participants earned reputation badges
   • 200 submissions were processed with the AI assistance layer
     Metrics captured included reviewer‑time per paper, review quality (measured by senior‑reviewer agreement), and author satisfaction scores.
5. Statistical Validation – Paired t‑tests and mixed‑effects models assessed the significance of observed improvements against baseline data from previous years.

Results & Findings

• Reviewer pool growth: The training program attracted 62 % more first‑time reviewers compared with the previous season.
• Bias mitigation: AI‑generated checklists surfaced missing reproducibility artifacts, reducing “needs more experiments” comments by 21 %.
• Community reception: 84 % of authors reported that AI‑augmented reviews were “clearer” and “more actionable.”

Practical Implications

• Conference organizers can adopt the open‑source training modules to quickly expand their reviewer base, especially for emerging sub‑domains (e.g., AI‑driven SE tools).
• Tool vendors have a ready‑made API for the AI‑assistance layer (paper summarization, methodological checklists) that can be integrated into submission platforms like EasyChair or OpenReview, cutting reviewer fatigue and speeding up decision cycles.
• Researchers gain a transparent reputation system (badges, reviewer‑credit tokens) that can be cited on CVs, encouraging more senior scholars to allocate time for reviewing.
• Industry partners can sponsor reviewer‑credit tokens, creating a virtuous loop where practitioners receive early access to cutting‑edge research while helping sustain the review pipeline.
• Long‑term sustainability: By lowering the per‑paper review cost and widening participation, SE conferences can maintain low acceptance‑rate standards without sacrificing turnaround speed, preserving the field’s credibility and growth trajectory.

Limitations & Future Work

• Generalizability – The pilot focused on top‑tier SE conferences; results may differ for journals or niche workshops with different reviewer cultures.
• AI reliability – While the AI tools reduced workload, occasional hallucinations in summarization were observed; a human‑in‑the‑loop verification step remains essential.
• Incentive bias – Reputation badges could inadvertently favor quantity over quality; future designs must incorporate robust quality‑control metrics.
• Scalability of mentorship – Pairing novices with senior mentors works at pilot scale but may require automated matching algorithms for larger conferences.

Future research directions include extending the framework to interdisciplinary venues, exploring blockchain‑based reviewer credit systems, and conducting longitudinal studies to measure the impact of sustained AI assistance on review quality over multiple conference cycles.

Authors

• Esteban Parra
• Sonia Haiduc
• Preetha Chatterjee
• Ramtin Ehsani
• Polina Iaremchuk

Paper Information

• arXiv ID: 2601.21761v1
• Categories: cs.SE
• Published: January 29, 2026
• PDF: Download PDF