A computer-implemented method for converting an architecture specification of a system to one or more functional performance requirements textual statements describing one or more functions of the system is provided. The computer-implemented method includes obtaining the architecture specification based on a predefined architecture model diagram type, wherein the architecture specification comprises a plurality of interconnected functional portions that describe the one or more functions of the system; mapping one or more of the plurality of interconnected functional portions to one or more portions of the one or more functional performance requirements textual statements based on a predefined format of a template functional performance requirements textual statement; and creating the one or more functional performance requirements textual statements based on the mapping.

A computer system including a memory, a network interface, and a processor is provided. The processor is configured to receive, via the network interface, one or more design attributes of a microapp from a microapp development tool hosted by an endpoint device, the one or more design attributes comprising an identifier of a system of record configured to supply data to the microapp; execute a machine learning process trained, using data regarding microapp usage within an organization, to predict at least one user engagement metric for the microapp based on the one or more design attributes; and transmit, via the network interface, the at least one user engagement metric to the microapp development tool hosted by the endpoint device.

A computer-implemented method classifies and creates a container file based on non-functional parameters. The method includes analyzing, by a learning model, a codebase. The codebase includes code for one or more applications. The method also includes identifying, based on the analyzing, a set of functional requirements for each application and a set of non-functional parameters. The method further includes classifying a first application of the one or more applications with a first non-functional parameter. The method includes generating a first container file for the first application. The first container file includes the functional requirements for the first application and the first non-functional parameter. The method further includes creating a first container from the first container file.

A computer-implemented method classifies and creates a container file based on non-functional parameters. The method includes analyzing, by a learning model, a codebase. The codebase includes code for one or more applications. The method also includes identifying, based on the analyzing, a set of functional requirements for each application and a set of non-functional parameters. The method further includes classifying a first application of the one or more applications with a first non-functional parameter. The method includes generating a first container file for the first application. The first container file includes the functional requirements for the first application and the first non-functional parameter. The method further includes creating a first container from the first container file.

A system and method for estimating software project delivery details is disclosed. The disclosed method and system accurately predicts project delivery details (e.g., person-days, effort, full-time equivalent (FTE), etc.) from input text (e.g., text describing a software project requirements) and project input parameters (e.g., variables, such as logical entities, transactions, individual/team proficiency, team level, duration of project, technology, industry domain, etc.) corresponding to a project scenario. In addition to predicting project delivery details, the disclosed system and method can provide a what-if analysis engine that allows a user to understand the possibilities of project scenarios having certain output.

A system and method for estimating software project delivery details is disclosed. The disclosed method and system accurately predicts project delivery details (e.g., person-days, effort, full-time equivalent (FTE), etc.) from input text (e.g., text describing a software project requirements) and project input parameters (e.g., variables, such as logical entities, transactions, individual/team proficiency, team level, duration of project, technology, industry domain, etc.) corresponding to a project scenario. In addition to predicting project delivery details, the disclosed system and method can provide a what-if analysis engine that allows a user to understand the possibilities of project scenarios having certain output.

A method, system, and computer program product for implementing automated system capacity optimization is provided. The method includes retrieving from plug-in components running on a plurality of hardware and software sources, metrics data associated with the plug-in components. The metrics data is cross-referenced with respect to operational sizing recommendations for each plug-in component based on aggregated disparate sizing guidelines and resulting software code modules are generated. Software and hardware requirements for enabling target computing components are determined based on results of executing the software code modules and operational functionality of the target computing components are enabled in accordance with the software and hardware requirements.

A method, system, and computer program product for implementing automated system capacity optimization is provided. The method includes retrieving from plug-in components running on a plurality of hardware and software sources, metrics data associated with the plug-in components. The metrics data is cross-referenced with respect to operational sizing recommendations for each plug-in component based on aggregated disparate sizing guidelines and resulting software code modules are generated. Software and hardware requirements for enabling target computing components are determined based on results of executing the software code modules and operational functionality of the target computing components are enabled in accordance with the software and hardware requirements.

Certain aspects involve models for generating code executed on data-processing platforms. One example involves receiving an electronic data-processing model, which generates an analytical output from input attributes weighted with respective modeling coefficients. A target data-processing platform is identified that requires bin ranges for the modeling coefficients and reason codes for the input attributes. Modeling code is generated that implements the electronic data-processing model with the bin ranges and the reason codes. The processor outputs executable code that implements the electronic data-processing model.

Certain aspects involve models for generating code executed on data-processing platforms. One example involves receiving an electronic data-processing model, which generates an analytical output from input attributes weighted with respective modeling coefficients. A target data-processing platform is identified that requires bin ranges for the modeling coefficients and reason codes for the input attributes. Modeling code is generated that implements the electronic data-processing model with the bin ranges and the reason codes. The processor outputs executable code that implements the electronic data-processing model.