Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap

arXiv:2505.19625 · cs.SE, cs.AI · Submitted 2025-05-26 · Read on arXiv

cs.SE, cs.AI

Submitted: 2025-05-26

Updated: 2026-09-24

Code: https://github.com/EvoSuite/evosuite

Terminology

• Software Engineering — 115× in this paper
• Foundation Model — 23× in this paper · explained in 3 episodes
• Foundation Models — 22× in this paper · explained in 7 episodes
• Verification — 18× in this paper · explained in 2 episodes
• Search-Based Software Engineering — 16× in this paper
• Autonomous Driving Systems — 13× in this paper
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Hallucination — 12× in this paper · explained in 3 episodes
• Prompt Engineering — 12× in this paper · explained in 4 episodes
• Core — 10× in this paper · explained in 2 episodes
• Multi-objective Optimization — 10× in this paper · explained in 4 episodes
• International Conference on Software Engineering — 7× in this paper
• Score Function — 6× in this paper · explained in 4 episodes
• Pruning — 5× in this paper · explained in 4 episodes
• Adaptability — 4× in this paper · explained in 1 episode
• Artificial Intelligence — 4× in this paper
• Digital Twin — 4× in this paper · explained in 4 episodes
• Few-Shot Learning — 4× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• ACM International Workshop on Genetic Improvement — 3× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Contextual Understanding — 3× in this paper · explained in 2 episodes
• Continuous Integration — 3× in this paper
• Foundation Models (FMs) — 3× in this paper · explained in 2 episodes
• Hallucination Detection — 3× in this paper · explained in 2 episodes
• Internet of Things — 3× in this paper
• AI Foundation Model — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• International Symposium on Software Reliability Engineering — 2× in this paper
• Object Management Group — 2× in this paper
• Objective Function — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Unified Modeling Language — 2× in this paper
• Analyzer — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• LLMs (Large Language Models) — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Workshop on Search-Based Software Testing — 1× in this paper

Sources

• On the Opportunities and Risks of Foundation Models
• Evaluating Large Language Models Trained on Code
• A Generic Approach to Fix Test Flakiness in Real-World Projects
• RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search
• Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
• A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
• A Survey of Automatic Prompt Engineering: An Optimization Perspective
• Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization
• Explaining Genetic Programming Trees using Large Language Models
• A Survey of Hallucination in Large Foundation Models
• Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges
• Generative AI for Testing of Autonomous Driving Systems: A Survey
• Prompting for Performance: Exploring LLMs for Configuring Software
• Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm
• Vision Language Action Models in Robotic Manipulation: A Systematic Review
• VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model
• Quantum Artificial Intelligence for Software Engineering: the Road Ahead
• Large Language Models in Fault Localisation
• The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
• A Survey on Large Language Models for Software Engineering

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