Application interface viability network
Systems, computer program products, and methods are described herein for viability testing of applications in a distributed computing environment. An example system may include a plurality of generative AI subsystems, each associated with a corresponding application within the distributed environment. Each generative AI subsystem may be configured to analyze the functional accessibility of the associated application and generate a first output based on the analysis to facilitate user interaction with the application. The system may further include a primary generative AI subsystem that may be configured to receive the first outputs from the generative AI subsystems, determine dependency information among the applications based on the first outputs, and generate a second output based on at least the dependency information. The second output may include documentation for data interactions among the applications, providing insights into dependencies, data flows, and connectivity within the distributed computing environment.
1 . A system for viability testing of applications in a distributed computing environment, the system comprising: a plurality of generative artificial intelligence (AI) subsystems, wherein each generative AI subsystem is associated with a corresponding application within the distributed computing environment, wherein each generative AI subsystem is configured to: analyze functional accessibility of the corresponding application, comprising: determining one or more application interaction patterns based on analyzing application navigability, application interface components, and common user actions; performing user interaction data analysis to determine one or more application functions that are associated with a user challenge, based on navigation flows, click frequencies, and error rates associated with the corresponding application; simulating user interactions associated with the corresponding application, comprising determining usage data associated with time on task, success rate, and number of steps for completion associated with the one or more application functions; and identifying one or more high complexity user interface areas associated with an application user interface of the corresponding application, based on at least a layout and a density of user interface elements of the application user interface; and generate a first output based on at least the analysis, wherein the first output comprises documentation for interacting with the corresponding application; and a primary generative AI subsystem operatively coupled to the plurality of generative AI subsystems, wherein the primary generative AI subsystem is configured to: receive the first outputs from the plurality of generative AI subsystems; determine dependency information associated with application dependencies among the plurality of applications based on the first outputs, comprising application data flow requirements, application communication protocols, application task sequences and application resource sharing mechanisms, wherein determining dependency information comprises: classifying the plurality of applications based on an application type, comprising functional dependencies, data sharing dependencies, access control dependencies, and processing sequence dependencies; and analyzing data flow requirements and communication protocols between the plurality of applications to categorize the application dependencies by data dependencies sequential task dependences, and/or conditional dependencies; and generate a second output based on at least the dependency information, wherein the second output comprises documentation for data interaction among the applications, and a machine learning (ML) subsystem, wherein the ML subsystem is configured to: generate one or more test scripts associated with testing the application dependencies among the plurality of applications, based on at least the application data flow requirements and application communication protocols, wherein the one or more test scripts are structured to simulate user interactions, application responses and data exchanges associated with the plurality of applications.
2 . The system of claim 1 , wherein the one or more test scripts are configured to evaluate interface viability and functional compatibility between the plurality of applications in the distributed computing environment, wherein the ML subsystem is configured to: identify, via the one or more test scripts, one or more compatibility aspects associated with the plurality of applications, comprising data formats, synchronization of shared resources, and responsiveness to cross-application tasks; and determine one or more application interaction issues comprising connectivity, resource contention, and/or operation delays.
3 . The system of claim 1 , wherein, in analyzing the functional accessibility, each generative AI subsystem is configured to implement a usability assessment framework on the corresponding application, wherein the usability assessment framework comprises at least one of the user interaction data analysis, heuristic evaluation, natural language processing (NLP) for user feedback, the simulated user interactions, automated usability testing, interface element recognition and complexity analysis, or predictive usability modeling.
4 . The system of claim 3 , wherein, in analyzing the functional accessibility, each generative AI subsystem is configured to:
assess, using the usability assessment framework, user interface elements of the corresponding application to identify navigability, usability, and interaction pathways;
evaluate performance metrics associated with user interactions based on the navigability and interaction pathways to determine operational efficiency;
analyze error handling mechanisms and user feedback prompts within the corresponding application based on the performance metrics to assess clarity and accessibility of functionality;
determine application workflows and dependencies that impact user access to specific functions based on the analysis of error handling mechanisms and the user feedback prompts; and
generate accessibility metrics from the application workflows and dependencies, wherein the accessibility metrics comprise a quantitative representation of the corresponding application's functional accessibility for use.
5 . The system of claim 1 , wherein the first output comprises step-by-step instructions for performing common tasks within the corresponding application based on the identified navigability and interaction pathways.
6 . The system of claim 1 , wherein the first output further comprises visual aids, including annotated screenshots, flow diagrams, or user interface mappings, to enhance user understanding of interactions within the corresponding application.
7 . The system of claim 1 , wherein each generative AI subsystem is configured to analyze the functional accessibility of the corresponding application in response to a change in the corresponding application.
8 . The system of claim 7 , wherein the change in the corresponding application comprises at least one of an update to the corresponding application's user interface components, including modifications to layout, color scheme, or interaction elements, an enhancement or addition of new features and/or functionalities within the corresponding application, a modification to application workflows or task sequences that impacts user interaction pathways, a change in application dependencies of the corresponding application, wherein the corresponding application requires communication or data exchange with additional systems or components within the distributed computing environment, a revision of error handling mechanisms, including updates to error messages or feedback prompts presented to users, an adjustment to performance parameters, such as response times or resource allocation, that impacts the corresponding application's operational efficiency, a security update that affects user access controls, permissions, or authentication processes within the corresponding application, or an alteration to accessibility features.
9 . The system of claim 1 , wherein the first output comprises interactive documentation that adapts based on user role, experience level, or recent interactions within the corresponding application, providing tailored guidance for navigating and using the corresponding application.
10 . The system of claim 1 , wherein the documentation in the first output comprises at least contextual tooltips and embedded help elements that appear within the corresponding application's interface, providing users with on-demand assistance and instructions specific to each interface component or function.
11 . The system of claim 1 , wherein the documentation for data interaction among the applications comprises a visual interaction map that illustrates data flow paths, data dependencies, and interaction sequences among the applications, enabling users to understand data relationships and identify potential bottlenecks in data exchanges.
12 . The system of claim 1 , wherein the documentation for data interaction among the applications comprises a data integration protocol that specifies data formatting standards, communication methods, and security requirements for data exchanges between applications, providing standardized instructions for maintaining compatibility and secure data interactions.
13 . The system of claim 1 , wherein the ML subsystem is configured to:
implement the one or more test scripts at the plurality of applications;
generate test results based on implementation of the one or more test scripts; and
in response to detecting one or more issues in the test results, dynamically adjust the one or more test scripts.
14 . A method for viability testing of applications in a distributed computing environment, the method comprising: analyzing, using each generative artificial intelligence (AI) subsystem of a plurality of generative AI subsystems, functional accessibility of a corresponding application, comprising: determining one or more application interaction patterns based on analyzing application navigability, application interface components, and common user actions; performing user interaction data analysis to determine one or more application functions that are associated with a user challenge, based on navigation flows, click frequencies, and error rates associated with the corresponding application; simulating user interactions associated with the corresponding application, comprising determining usage data associated with time on task, success rate, and number of steps for completion associated with the one or more application functions; and identifying one or more high complexity user interface areas associated with an application user interface of the corresponding application, based on at least a layout and a density of user interface elements of the application user interface; generating, using each generative artificial intelligence (AI) subsystem, a first output based on at least the analysis, wherein the first output comprises documentation for interacting with the corresponding application; receiving, using a primary generative AI subsystem, the first outputs from the plurality of generative AI subsystems; determining, using the primary generative AI subsystem, dependency information associated with operational application dependencies among the plurality of applications based on the first outputs, comprising application data flow requirements, application communication protocols, application task sequences and application resource sharing mechanisms, wherein determining dependency information comprises: classifying the plurality of applications based on an application type, comprising functional dependencies, data sharing dependencies, access control dependencies, and processing sequence dependencies; and analyzing data flow requirements and communication protocols between the plurality of applications to categorize the application dependencies by data dependencies sequential task dependences, and/or conditional dependencies; generating, using the primary generative AI subsystem, a second output based on the dependency information, wherein the second output comprises documentation for data interaction among the applications; and generating, via a machine learning (ML) subsystem, one or more test scripts associated with testing the application dependencies among the plurality of applications, based on at least the application data flow requirements and application communication protocols, wherein the one or more test scripts are structured to simulate user interactions, application responses and data exchanges associated with the plurality of applications.
15 . The method of claim 14 , wherein the one or more test scripts are configured to evaluate interface viability and functional compatibility between the plurality of applications in the distributed computing environment, further comprising: identifying, via the one or more test scripts, one or more compatibility aspects associated with the plurality of applications, comprising data formats, synchronization of shared resources, and responsiveness to cross-application tasks; and determining one or more application interaction issues comprising connectivity, resource contention, and/or operation delays.
16 . The method of claim 15 , wherein the analyzing the functional accessibility further comprises implementing a usability assessment framework on the corresponding application, wherein the usability assessment framework comprises at least one of the user interaction data analysis, heuristic evaluation, natural language processing (NLP) for user feedback, the simulated user interactions, automated usability testing, interface element recognition and complexity analysis, or predictive usability modeling.
17 . The method of claim 16 , wherein analyzing the functional accessibility further comprises:
assessing, using the usability assessment framework, user interface elements of the corresponding application to identify navigability, usability, and interaction pathways;
evaluating performance metrics associated with user interactions based on the navigability and interaction pathways to determine operational efficiency;
analyzing error handling mechanisms and user feedback prompts within the corresponding application based on the performance metrics to assess clarity and accessibility of functionality;
determining application workflows and dependencies that impact user access to specific functions based on the analysis of error handling mechanisms and the user feedback prompts; and
generating accessibility metrics from the application workflows and dependencies, wherein the accessibility metrics comprise a quantitative representation of the corresponding application's functional accessibility for use.
18 . A computer program product for viability testing of applications in a distributed computing environment, the computer program product comprising a non-transitory computer-readable medium comprising code configured to cause an apparatus to: analyze, using each generative artificial intelligence (AI) subsystem of a plurality of generative AI subsystems, functional accessibility of a corresponding application, comprising: determining one or more application interaction patterns based on analyzing application navigability, application interface components, and common user actions; performing user interaction data analysis to determine one or more application functions that are associated with a user challenge, based on navigation flows, click frequencies, and error rates associated with the corresponding application; simulating user interactions associated with the corresponding application, comprising determining usage data associated with time on task, success rate, and number of steps for completion associated with the one or more application functions; and identifying one or more high complexity user interface areas associated with an application user interface of the corresponding application, based on at least a layout and a density of user interface elements of the application user interface; generate, using each generative artificial intelligence (AI) subsystem, a first output based on at least the analysis, wherein the first output comprises documentation for interacting with the corresponding application; receive, using a primary generative AI subsystem, the first outputs from the plurality of generative AI subsystems; determine, using the primary generative AI subsystem, dependency information associated with operational application dependencies among the plurality of applications based on the first outputs, comprising application data flow requirements, application communication protocols, application task sequences and application resource sharing mechanisms, wherein determining dependency information comprises: classifying the plurality of applications based on an application type, comprising functional dependencies, data sharing dependencies, access control dependencies, and processing sequence dependencies; and analyzing data flow requirements and communication protocols between the plurality of applications to categorize the application dependencies by data dependencies sequential task dependences, and/or conditional dependencies; generate, using the primary generative AI subsystem, a second output based on the dependency information, wherein the second output comprises documentation for data interaction among the applications; and generate, via a machine learning (ML) subsystem, one or more test scripts associated with testing the application dependencies among the plurality of applications, based on at least the application data flow requirements and application communication protocols, wherein the one or more test scripts are structured to simulate user interactions, application responses and data exchanges associated with the plurality of applications.
19 . The computer program product of claim 18 , wherein the one or more test scripts are configured to evaluate interface viability and functional compatibility between the plurality of applications in the distributed computing environment, wherein the code further causes the apparatus to: identify, via the one or more test scripts, one or more compatibility aspects associated with the plurality of applications, comprising data formats, synchronization of shared resources, and responsiveness to cross-application tasks; and determine one or more application interaction issues comprising connectivity, resource contention, and/or operation delays generate.
20 . The computer program product of claim 18 , wherein the analyzing the functional accessibility further comprises implementing a usability assessment framework on the corresponding application, wherein the usability assessment framework comprises at least one of the user interaction data analysis, heuristic evaluation, natural language processing (NLP) for user feedback, the simulated user interactions, automated usability testing, interface element recognition and complexity analysis, or predictive usability modeling.