Disclosed herein are system, method, and computer program product embodiments for facilitating access to and incorporation of APIs within an application during development, while ensuring that the API satisfies quality constraints. These quality constraints are controlled by the application of rules, which result in a maturity score for the API, in accordance with embodiments. These rules may be provided by a variety of sources for use in scoring the API at various stages. For example, a developer group may control access to developers within the group by applying a ruleset that restricts APIs for applications written by the developer group to a threshold score. This threshold score can limit or restrict APIs that are offered to a developer by an API exchange. Additionally, a ruleset may be applied to enforce a minimum score for submission of an API for inclusion within an API exchange.

In some examples, system behavior profiling-based dynamic competency analysis may include identifying a plurality of software generation entities that have contributed to a module of a system, and generating an index to associate each software generation entity of the plurality of software generation entities. Execution links may be extracted from execution traces of the system, and an execution competency list may be generated. A dynamic competency score may be generated for each software generation entity for the system, and an overall dynamic competency score and a combined competency score may be determined. A software generation entity role may be obtained for a new application, and a software generation entity of the plurality of software generation entities may be identified to perform the software generation entity role. Development of the new application may be implemented using the identified software generation entity.

In some examples, system behavior profiling-based dynamic competency analysis may include identifying a plurality of software generation entities that have contributed to a module of a system, and generating an index to associate each software generation entity of the plurality of software generation entities. Execution links may be extracted from execution traces of the system, and an execution competency list may be generated. A dynamic competency score may be generated for each software generation entity for the system, and an overall dynamic competency score and a combined competency score may be determined. A software generation entity role may be obtained for a new application, and a software generation entity of the plurality of software generation entities may be identified to perform the software generation entity role. Development of the new application may be implemented using the identified software generation entity.

A method of providing a secure development operations system that can accommodate multiple projects, multiple tenants, and multiple security classifications includes creating a first sub-program with the first sub-program being part of a first project and designating the first sub-program with a first security classification label. The method also includes transferring the first sub-program to a first repository of the development operations system with the first repository being configured to contain sub-programs associated with the first project and transferring a copy of the first sub-program to a second repository of the development operations system. The second repository is configured to contain sub-programs from multiple projects and sub-programs that have different security classification labels.

A method of providing a secure development operations system that can accommodate multiple projects, multiple tenants, and multiple security classifications includes creating a first sub-program with the first sub-program being part of a first project and designating the first sub-program with a first security classification label. The method also includes transferring the first sub-program to a first repository of the development operations system with the first repository being configured to contain sub-programs associated with the first project and transferring a copy of the first sub-program to a second repository of the development operations system. The second repository is configured to contain sub-programs from multiple projects and sub-programs that have different security classification labels.

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

Various methods, apparatuses/systems, and media for developing an application are disclosed. A processor converts requirements data into a first semantic context data, the requirements data describing intended tasks required for developing an application; converts the first semantic context data into a first semantic context vector; accesses a database that stores source code corresponding to implementation of the intended tasks required for developing the application; converts the source code into a second semantic context data; converts the second semantic context data into a second semantic context vector; compares the first semantic context vector and the second semantic context vector; automatically generates, in response to comparing, a similarity score that indicates how much the source code and the requirements data are in line with each other; and executes development of the application when it is determined that the similarity score is equal to or more than a predetermined threshold value.

An architectural diagram recommendation engine is implemented in a data processing system for software architectural diagram analysis and recommendation. The architectural diagram recommendation engine analyzes a software requirements specification document using natural language processing to identify functional requirements and security requirements. The architectural diagram recommendation engine analyzes a digital software architectural diagram image to identify functional components and identifies one or more discrepancies between the functional components of the digital software architectural diagram image and the functional requirements or security requirements. The architectural diagram recommendation engine generates an alert concerning the one or more discrepancies and presents the alert in association with the digital software architectural diagram image.

An architectural diagram recommendation engine is implemented in a data processing system for software architectural diagram analysis and recommendation. The architectural diagram recommendation engine analyzes a software requirements specification document using natural language processing to identify functional requirements and security requirements. The architectural diagram recommendation engine analyzes a digital software architectural diagram image to identify functional components and identifies one or more discrepancies between the functional components of the digital software architectural diagram image and the functional requirements or security requirements. The architectural diagram recommendation engine generates an alert concerning the one or more discrepancies and presents the alert in association with the digital software architectural diagram image.