In some examples, a method includes receiving an artificial intelligence (AI) system scenario definition file from a user, parsing the definition file and building an application workflow graph for the AI system, and mapping the application workflow graph to an execution pipeline. In some examples, the method further includes automatically generating, from the workflow graph, application executable binary code implementing the AI system, and outputting the application executable binary code to the user. In some examples, the execution pipeline includes one or more building blocks, and the method then further includes collecting running performance of each of the building blocks of the execution pipeline in a runtime environment.

An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

Techniques are described herein for training a machine learning model and using the trained machine learning model to more accurately determine alignments between matching/corresponding nodes of predecessor and successor graphs representing predecessor and successor source code snippets. A method includes: obtaining a first abstract syntax tree that represents a predecessor source code snippet and a second abstract syntax tree that represents a successor source code snippet; determining a mapping across the first and second abstract syntax trees; obtaining a first control-flow graph that represents the predecessor source code snippet and a second control-flow graph that represents the successor source code snippet; aligning blocks in the first control-flow graph with blocks in the second control-flow graph; and applying the aligned blocks as inputs across a trained machine learning model to generate an alignment of nodes in the first abstract syntax tree with nodes in the second abstract syntax tree.

Processor cores using content object identifiers for routing and computation are disclosed. One method includes executing a complex computation using a set of processing cores. The method includes routing a set of content objects using a set of content object identifiers and executing a set of instructions. The set of instructions are defined using a set of operand identifiers. The operand identifiers represent content object identifiers in the set of content object identifiers. The content objects can be routed according to a named data networking (NDN) or content-centric networking (CCN) paradigm with the content object identifiers mentioned above serving as the names for the computation data being routed by the network.

An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

A script analysis platform may obtain a script associated with content wherein the script includes one or more functions that include one or more expressions. The script analysis platform may parse the script to generate a data structure and may traverse the data structure to determine the one or more functions and to determine properties of the one or more expressions, wherein traversing the data structure includes evaluating one or more constant sub-expressions of the one or more expressions. The script analysis platform may analyze the properties of the one or more expressions to determine whether the script exhibits malicious behavior. The script analysis platform may cause an action to be performed concerning the script or the content based on determining whether the script exhibits malicious behavior.

According to one embodiment, a method that supports queries deploying operators based on multiple programming languages is described. A sequence of operators associated with a query is identified, where the sequence of operators includes at least two neighboring operators including a first operator based on a first programming language and a second operator based on a second programming language that is different from the first programming language. Thereafter, a schema associated with the first operator and a schema associated with the second operator is determined along with the compatibility between the schema of the first operator and the schema of the second operator. A query error message is generated in response to incompatibility between the first operator schema and the second operator schema. Compatibility is determined when an output generated by execution of the first operator provides machine data needed as input for execution of the second operator.

One example method includes receiving a code request, parsing code associated with the code request, storing, in a staging database, a portion of the code, traversing a codebase to identify any code in the codebase that matches the portion of the code, and when code is found in the codebase that matches the portion of the code, incrementing a green count and pushing the portion of the code to a redundant code bin, and when no code is found in the codebase that matches the portion of the code, incrementing a red counter and updating the codebase to include the portion of the code.

Input data for an operating system command of an automation process is received. The operating system command is generated based on the received input data. The generated operating system command is parsed to identify one or more metrics. The identified one or more metrics are automatically evaluated to determine a security risk associated with the generated operating system command.

Code search is used to support various features. Code segments may be indexed using a code structure representation of the code segment. Code segments may be compared for similarity to identify a match with an input code segment using a comparison of logic trees generated for the input code segment and a stored code segment in an entry with a matching index value.