The present invention concerns a method for verifying traceability of first code instructions in a procedural programming language generated from second code instructions in a modelling language, characterised in that it comprises the implementation, by a piece of equipment (1), of steps of: (a) Syntactic analysis: o of the first instructions so as to generate an AST, and o of the second instructions so as to generate an MDT; (b) Semantic analysis: o Of the AST so as to identify patterns representative of basic functional blocks of the first instructions; o Of the MDT so as to identify characteristic properties of basic functional blocks of the second instructions; (c) Matching, pairwise, the identified basic functional blocks, and confirming the traceability of first code instructions only if: o for each block of the first instructions, there is a functionally equivalent block in the second instructions, and o for each block of the second instructions, there is a functionally equivalent block in the first instructions.

Features are disclosed for dynamically provisioning an application stack using a set of infrastructure resources. A computing device can receive an infrastructure template from an administrative device. Based on the infrastructure template, the computing device can determine a schema and an infrastructure as code. Using the schema, the computing device can receive a specification file from a developer device or an administrative device. The specification file can be a service specification file or an environment specification file. The computing device can inject the values of the specification file into the infrastructure as code. The computing device can use the injected infrastructure as code to generate and deploy the application stack to the developer device.

An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

Systems, computer program products, and methods are described herein for implementing data analytics in a mainframe environment. The present invention is configured to determine one or more data analytics resources associated with natural language processing algorithms; initiate one or more compiler protocols on the one or more data analytics resources to build one or more executable code for the one or more data analytics resources capable of being executed on a mainframe environment; establish a communication link with a job control language (JCL) subsystem associated with the mainframe environment; transmit the one or more executable code for the one or more data analytics resources to the JCL subsystem; generate one or more job control statements configured to be executable on the mainframe environment; generate a log of the one or more job control statements; and initiate an execution of the one or more job control statements on the mainframe environment.

Source code of a graph analysis program expressed in a platform-independent language which supports linear algebra primitives is obtained. An executable version of the program is generated, which includes an invocation of a function of a parallel programming library optimized for a particular hardware platform. A result of executing the program is stored.

A qubit value change monitor is disclosed. An initial qubit value of a qubit in superposition is determined based on a first plurality of readings of the qubit. Subsequent to determining the initial qubit value, a current first qubit value is determined based on a second plurality of readings of the qubit. It is determined that the initial first qubit value differs from the current first qubit value. Responsive to determining that the initial first qubit value differs from the current first qubit value, a changed qubit action is initiated.

A system and method to apply deep learning techniques to an automation engineering environment are provided. Big code files and automation coding files are retrieved by the system from public repositories and private sources, respectively. The big code files include examples general software structure examples to be utilized by the method and system to train advanced automation engineering software. The system represents the coding files in a common space as embedded graphs which a neural network of the system uses to learn patterns. Based on the learning, the system can predict patterns in the automation coding files. From the predicted patterns executable automation code may be created to augment the existing automation coding files.

An automated system for resolving program merges uses a sequence-to-sequence supervised machine learning model trained from developer-resolved merge conflicts to learn to predict a merge resolution to resolve a three-way program merge. The model utilizes an embedding of the merge tuple (A, B, O) which represents the program syntax, program semantics and the intent of the program inputs. The model uses a pointer mechanism to construct the resolved program in terms of the lines of source code found in the input programs.

Systems, computer program products, and methods are described herein for continuous cognitive code logic detection and prediction using machine learning techniques. The present invention is configured to receive, from a user input device, source code scripts and target code scripts for functional code logic components of a full stack, wherein the source code scripts and the target code scripts are associated with one or more tiers; generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack; train, using a machine learning algorithm, a machine learning model using the training dataset; determine a prediction accuracy associated with the machine learning model; determine that the prediction accuracy is greater than a predetermined threshold; and deploy the machine learning model on unseen source code scripts.

A method may include obtaining one or more software-repository packages. A programming-language function may be extracted from the one or more software-repository packages. A curation resource associated with the programming-language function may be identified. The curation resource may include descriptive information related to the programming-language function. The method may include generating a code description corresponding to the programming-language function based on the curation resource. A function-comment pair that includes the programming-language function and the generated code description may be determined. A programming language corpus that includes the one or more software-repository packages may be generated and augmented by the function-comment pair. The method may include training a machine learning model using the programming language corpus.