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high infrastructure costs. We will explore (1) learning-based techniques (e.g., LLMs, agents) to capture the intent behind code changes, (2) defining new metrics for test "quality" that go beyond code
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to establish a roadmap, (2) developing models and benchmarks for LLM-based refactoring, (3) designing autonomous agents, and (4) conducting studies to analyse real-world impact. We are committed to creating a
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. Treiber, the existing “Intelligent Agent Model” (IAM) for directional disordered traffic flow will be generalized to meet the above objectives (working name IAM2d). The main task is to simulate and
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, specifically modelling the complex interrelations among infrastructure, human operators, and organizational structures using dynamic graphs, system dynamics, Agent Based Models, and discrete event simulations