Abstract: Techniques are described for analyzing software application dependencies in an enterprise software system using a knowledge graph. A computer-implemented method includes transforming software data into nodes and edges according to a predefined graph schema. Each node represents an entity within a software architecture, each edge represents a relationship between entities, and entities include software components and infrastructure resources. The method also includes constructing a knowledge graph in a graph database from the nodes and edges. The knowledge graph integrates source code data with runtime dependency data. The method includes analyzing the software architecture by executing, using software agents, graph traversal queries against the knowledge graph to determine dependency chains between entities, and determining an impact scope for a change or failure event by tracing upstream and downstream dependencies from an originating entity.

Abstract: Techniques are described for predicting resiliency of software applications. Techniques provide for obtaining one or more software construction variables, software operation variables, and an error rate associated with a first software application. This includes training a machine learning model to predict a resiliency of a particular software application using the software construction variables, the software operation variables, and the error rate for each of the plurality of first software applications. A software construction variable and a software operation variable associated with the second software application are obtained. The trained machine learning model is applied to the software construction variable and the software operation variable associated with the second software application to predict an error rate for the second software application.

Abstract: Techniques are described for predicting resiliency of software applications. Techniques provide for obtaining one or more software construction variables, software operation variables, and an error rate associated with a first software application. This includes training a machine learning model to predict a resiliency of a particular software application using the software construction variables, the software operation variables, and the error rate for each of the plurality of first software applications. A software construction variable and a software operation variable associated with the second software application are obtained. The trained machine learning model is applied to the software construction variable and the software operation variable associated with the second software application to predict an error rate for the second software application.

Abstract: A sleeve (110) for use in metal casting has a sleeve body, where a longitudinal axis (B) has a side wall (116) formed around it, defining a sleeve interior. The sleeve body is open at a first end (118). A core (120) is formed integrally along an interior surface extends into the sleeve interior. The sleeve body and the core are formed of a gas-permeable refractory material. At least the core contains material for generating heat when heated by a molten metal. The core extends along the side wall from the first end to a second end. The core has a width that is constant or decreases in a radial direction away from the side wall, especially with a triangular profile, with a base thereof in contact with the side wall. The sleeve body is cylindrical or frustoconical, with a diameter that decreases from the open first end.