The field of program analysis is moving towards a more integrated approach, combining different types of analyses such as value-flow analysis and symbolic analysis. This integration enables more efficient and effective analysis of complex systems. Notably, the development of platforms that seamlessly combine these analyses is gaining traction. Furthermore, Dynamic Symbolic Execution (DSE) is being explored for various applications, including semantic difference analysis and grammar mining. DSE shows promise in analyzing component and connector architectures, but scalability remains a challenge. In the area of grammar mining, novel approaches are being proposed to automatically generate inputs, leveraging DSE and overcoming its limitations. These approaches have the potential to significantly improve the precision and recall of extracted grammars. Additionally, compositional automata learning is being applied to system integration, with a focus on componentwise learning. This approach exploits the internal compositional structure of systems to reduce complexity and improve learning efficiency. Some noteworthy papers in this area include: Desyan, a platform that integrates value-flow and symbolic analysis, providing a seamless and efficient way to blend different kinds of reasoning. Generating Inputs for Grammar Mining using Dynamic Symbolic Execution, a novel approach that leverages DSE to automatically generate inputs for grammar mining, achieving high precision and recall.