This paper propose a framework, called MicroMiner, a microservices identification approach that is based on static-relationship analyses between code elements as well as semantic analyses of the source code. It is relies on machine learning (ML) techniques

This work present SPHH, a novel self-supervised pretraining framework for heterogeneous HyperGNNs. Our method is able to effectively capture higher-order relations among entities in the data in a self-supervised manner. SPHH is consist of two self-supervised pretraining tasks that aim to simultaneously learn both local and global representations of the entities in the hypergraph by using informative representations derived from the hypergraph structure.