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A core data services (CDS) explain agent receives a CDS explain request to generate an explanation with respect to a CDS data model. A CDS explain process is orchestrated for the CDS data model using a CDS explain handler of the CDS explain agent. A validator of the CDS explain agent and advanced business application programming (ABAP) dictionary metadata is used to validate if a CDS entity exists and if the CDS entity can be explained. Using a prompt assembler of the CDS explain agent, a large language model (LLM) prompt is assembled by combining multiple prompt snippets that satisfy the explanation with respect to a CDS data model. Using a CDS explain request processor of the CDS explain agent, the LLM prompt is transmitted to an LLM. Using a post-processor of the CDS explain agent, relevant information of a response from the LLM is extracted.

Embodiments of the present disclosure include techniques for data aggregation and suggestion generation. In one embodiment, profile information from remote systems is cached in a local database. As searches are issued in the remote systems, event messages comprising search artifacts are generated and sent to the database. The search artifacts are aggregated and stored in the local database. A profile may be analyzed based on the stored data and suggestions are provided regarding attribute values that may be added to the profile to improve search performance.

A transaction which targets an object store employing an open table format (OTF) may be detected by a database execution engine. Next, the database execution engine performs a determination step to determine which OTF enhancement levels to apply to the detected transaction. Then, the database execution engine applies one or more OTF enhancement levels to the transaction based on the determination step. Next, the database execution engine causes the transaction to be processed with the applied one or more OTF enhancement levels. Then, the database execution engine returns a result of the transaction processing to a computing device.

A system and method include training a classification model to classify data based on first data associated with a first usage scenario, receiving second data associated with a second usage scenario inputting the second data to the classification model and receiving a likelihood of a first classification from the classification model, determining a similarity between the second data and a plurality of data associated with the second usage scenario, modifying the likelihood based on the determined similarity, determining a second classification of the second data based on the modified likelihood, and processing the second data according to the second classification of the second data.

The disclosure generally describes methods, software, and systems for identification of code embeddings. A source code fragment is received. Embedding models corresponding to the source code fragment are determined. Each of the embedding models converts the source code fragment into numerical vectors that capture a semantic meaning and a functionality of the source code fragment. Probes corresponding to properties of the source code fragment are determined. Each of the probes perform an analysis of the properties of the source code fragment. Performance metrics indicative of encapsulations of the probes in the embedding models are generated, using a machine learning model trained to process the embedding models and the probes. A ranked representation of the embedding models is generated using the performance metrics.

If the sizing for an application is incorrect, either too many resources are allocated for the application or too few. Traditionally, sizing is based on answers to a questionnaire, assumptions, data gathered after the application is deployed, or various combinations thereof. As discussed herein, directed testing is used to gather information about the hardware requirements of the application. The gathered information is used along with quality of service information to accurately size the application. A load unit component performs load testing of the application to find a linear dependence of the application on hardware resources for one or more dimensions of demand on the application. Based on the data gathered by the load unit component, a multi-variable linear regression is performed to determine a resource unit for the application and a number of instances of the resource unit to be allocated to the application.

In an example embodiment, resource utilization and resource assignment among StatefulSet instances is monitored and usage metrics are maintained. Multiple different StatefulSets are established, with each set having a different level of resource allocation. Individual instances can then be dynamically assigned/reassigned to the different StatefulSets based on resource utilization. An auto-scaler is provided to scale each StatefulSet to the needed number of instances.

Embodiments are described for a graphic-based programing system comprising a display, a memory, and at least one processor coupled to the display and the memory. The at least one processor is configured to receive a programing layout that comprises one or more function components and one or more connections between the one or more function components and store locations of the one or more function components in the memory. The at least one processor is further configured to determine that at least one function component out of the one or more function components corresponds to a first characteristic data and determine a working area in the programing layout. The at least one processor is further configured to determine that the at least one function component is outside the working area and display at least one indicator based on the location of the at least one function component.

The disclosure generally describes methods, software, and systems for generation of code property graph trees integrating control-flow and dataflow. A code property graph (CPG) of source code is received. The CPG includes a graph representation of the source code. The CPG merges information from an abstract syntax tree (AST) of the source code and a control flow graph of the source code. The CPG includes multiple CPG edges. A CPG edge of the plurality of CPG edges is added to the AST. The CPG edge includes a directed edge identifying a target node. An AST edge connecting the target node to a root node is identified, using the CPG edge. If a removal of the AST edge retains a tree property of the AST, the AST edge connecting the target node to a parent node is removed to generate a CPG tree including AST edges and CPG edges.

Embodiments of the present disclosure include techniques for analyzing and reducing dependent code. In one embodiment, application source code is linked to dependency code, some of which is used and some of which is not used. The application source code and dependency code are analyzed to produce a usage graph comprising nodes corresponding to functional code elements and edges between the nodes corresponding to calls between the code elements. Edges may be associated with weights that may be used to determine an amount of used in subtrees of the dependency code. Dependency code that is little used may be replaced by dependency code without indirect dependencies. Replacement dependency code may be autogenerated by extracting existing dependency code elements to build a prompt for a generative AI.