Call for papers, Search-Based Software Engineering in the Era of AI-Driven Systems

This special issue invites high-quality submissions presenting novel and original research in all areas of Search-Based Software Engineering (SBSE). We welcome contributions that explore new applications of SBSE, advance theoretical foundations, provide rigorous empirical evaluations or report on industrial experiences.

We encourage submissions covering a broad spectrum of techniques, from exact operational research methods to nature-inspired algorithms, local search meta-heuristics, and simulated annealing. Papers integrating search-based optimization with machine learning, reinforcement learning, and meta-learning for enhanced efficiency are particularly welcome.

We are especially interested in research that applies SBSE to previously unexplored software engineering challenges, as well as innovative approaches to optimizing and securing AI-driven software systems.

Topics of Interest include, but are not limited to:

•SBSE across the Software Engineering Lifecycle: Search-based techniques for requirements engineering, architecture optimization, testing, debugging, and maintenance.

•SBSE for AI-Driven Systems: Search-based optimization for deep learning model architectures, hyperparameter tuning, and adversarial robustness.

•SBSE for MLOps and Deployment Optimization: Resource allocation, cloud/edge AI efficiency, and automated pipeline configuration.

•Multi-Objective Optimization for AI-Powered Software: Balancing performance, cost, energy efficiency, and sustainability.

•Trustworthy and Explainable AI: Search-based approaches for fairness, interpretability, robustness, and security in AI-driven software.

•Automated Software Repair and Refactoring: AI-enhanced SBSE techniques for bug fixing, performance tuning, and maintainability.

•Industrial Applications and Case Studies: Real-world adoption of SBSE and AI-driven SBSE, including successes, challenges, and future research directions.

We invite both theoretical and empirical contributions and strongly encourage submissions that demonstrate the practical impact of SBSE on emerging AI-powered software engineering challenges.