Abstract: Refactoring a monolithic computer application can include transforming textual input into context-aware tokens represented by machine-processable data structures, the textual input acquired from text associated with a computer application having a monolithic architecture for implementing one or more application processes. Based on co-occurrence frequencies among the context aware tokens, one or more groupings of context-aware tokens can be determined. An association between each grouping and a code construct can be determined. Invocation sequences based on time series analyses of computer-generated usage data generated in response to execution of the one or more application processes can be generated, each invocation sequence linking two or more code constructs based on a time series analysis linking groupings that correspond to the linked code constructs.

Abstract: Refactoring a monolithic computer application can include transforming textual input into context-aware tokens represented by machine-processable data structures, the textual input acquired from text associated with a computer application having a monolithic architecture for implementing one or more application processes. Based on co-occurrence frequencies among the context aware tokens, one or more groupings of context-aware tokens can be determined. An association between each grouping and a code construct can be determined. Invocation sequences based on time series analyses of computer-generated usage data generated in response to execution of the one or more application processes can be generated, each invocation sequence linking two or more code constructs based on a time series analysis linking groupings that correspond to the linked code constructs.

Abstract: Refactoring a monolithic computer application can include transforming textual input into context-aware tokens represented by machine-processable data structures, the textual input acquired from multiple sources of text associated with a computer application having a monolithic architecture for implementing one or more application processes. Based on co-occurrence frequencies among the context aware tokens, one or more groupings of context-aware tokens can be determined. An association between each grouping and a code construct can be determined. Invocation sequences based on time series analyses of computer-generated usage data generated in response to execution of the one or more application processes can be generated, each invocation sequence linking two or more code constructs based on a time series analysis linking groupings that correspond to the linked code constructs.

Abstract: A method, computing system, and computer program product are provided. Items at a source location are detected and classified, with respect to fragility and perishability, based on characteristics of the each respective item and is performed by trained machine learning models. Item boundaries are predicted based on applying respective data regarding points on a surface of the each respective item to a trained second machine learning model to predict the item boundaries. The each respective item is classified into a respective group with respect to an available volume of the cargo space based on sensor data of the cargo space, the classified fragility and perishability, the predicted item boundaries, and a third machine learning model. An arrangement of the items in the cargo space is determined based on the group classifications and a corresponding destination location associated with the each respective item and is visualized relative to the cargo space.

Abstract: A computer-implemented method for representing and storing data in an extendable graph data structure using artificial intelligence includes obtaining business requirements data, business workflow data, and dictionary data. A relationship between one or more entities in the obtained business requirement data and the business workflow data is identified using the obtained dictionary data and applying a natural language processing technique and topic based trend identification from existing entities and associated attributes. An extendable graph data structure represented by an enriched adjacency matrix for the identified relationship between the one or more entities is generated and stored. One or more trends are identified using the stored graph data structure.

Abstract: A computer-implemented method for representing and storing data in an extendable graph data structure using artificial intelligence includes obtaining business requirements data, business workflow data, and dictionary data. A relationship between one or more entities in the obtained business requirement data and the business workflow data is identified using the obtained dictionary data and applying a natural language processing technique and topic based trend identification from existing entities and associated attributes. An extendable graph data structure represented by an enriched adjacency matrix for the identified relationship between the one or more entities is generated and stored. One or more trends are identified using the stored graph data structure.

Abstract: A method, computing system, and computer program product are provided. Items at a source location are detected and classified, with respect to fragility and perishability, based on characteristics of the each respective item and is performed by trained machine learning models. Item boundaries are predicted based on applying respective data regarding points on a surface of the each respective item to a trained second machine learning model to predict the item boundaries. The each respective item is classified into a respective group with respect to an available volume of the cargo space based on sensor data of the cargo space, the classified fragility and perishability, the predicted item boundaries, and a third machine learning model. An arrangement of the items in the cargo space is determined based on the group classifications and a corresponding destination location associated with the each respective item and is visualized relative to the cargo space.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for generating a recommendation for a composite computer application program from unstructured text. Unstructured text specifying functional requirements for a composite computer application program is received. The unstructured text is processed to generate topic metadata. The topics represent actions to be performed by the composite computer application program. Based on the generated topic metadata, a micro service is determined for performing each action. A recommendation for a sequence of microservices pertinent to the specified functional requirements is also determined, wherein each microservice is deployed in a separate container. Rules for synchronizing operations between the individual containers are specified. A recommendation for a deployable composite computer application program comprising the collection of individual containers and the specified rules is generated.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for generating a recommendation for a composite computer application program from unstructured text. Unstructured text specifying functional requirements for a composite computer application program is received. The unstructured text is processed to generate topic metadata. The topics represent actions to be performed by the composite computer application program. Based on the generated topic metadata, a micro service is determined for performing each action. A recommendation for a sequence of microservices pertinent to the specified functional requirements is also determined, wherein each microservice is deployed in a separate container. Rules for synchronizing operations between the individual containers are specified. A recommendation for a deployable composite computer application program comprising the collection of individual containers and the specified rules is generated.

Abstract: In creating recommendations for software upgrades in a computing system, a natural language processing module of a software upgrade analyzer system receives risk analysis reports and a corresponding current application source code. The natural language processing module processes the risk analysis report and the current application source code to identify an application programming interface (API) key set containing key terms. A support vector machine of the software upgrade analyzer system identifies key terms in the API key set. An association rule mining module of the software upgrade analyzer system calculates a support factor and a confidence factor for each key term combination of the key terms. The association rule mining module identifies a highest ranking key term combination based on the confidence factor for each key term combination. The software upgrade analyzer system outputs the highest ranking key term combination as a software upgrade recommendation.

Abstract: A computer-implemented method and system for analyzing a version upgrade to a software application using product documents by: identifying changes made by the version upgrade to an application programming interface (API) used by the software application from the product documents; comparing the identified changes made by the version upgrade to the application programming interface to source code and custom files for the software application, which includes performing natural language processing on the source code and custom files for the software application to analyze components of the source code and custom files that use the application programming interface; and generating an impact analysis report based on the identified changes made by the version upgrade to the application programming interface.