Towards A Sustainable Future for Peer Review in Software Engineering

2026-01-29

AI Summary

This work addresses the growing strain on the peer review system in software engineering, driven by a surge in paper submissions and a scarcity of qualified reviewers. To overcome the limitations of traditional models that rely heavily on a finite pool of expert reviewers, the study proposes a novel, sustainable tripartite paradigm integrating reviewer training, community-driven incentives, and judicious AI assistance. By establishing a structured reviewer development program, implementing incentive mechanisms to foster broad community participation, and carefully deploying AI tools to enhance both efficiency and quality, the authors construct a scalable, inclusive, and resilient peer review framework. This approach aims to substantially alleviate reviewer burden, improve review quality, and encourage broader engagement from the research community.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Mechanism Design

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.

Problem

• peer review
• software engineering
• reviewer shortage
• research sustainability
• scientific evaluation

Innovation

• peer review
• AI-assisted review
• reviewer training
• community incentives
• sustainable research