Automating code analysis, dependency mapping, and adaptive task execution with AI

1 . A method for automating code analysis, dependency mapping, and adaptive task execution with AI, comprising:

receiving input data at a server platform, the input data including a codebase and associated documentation;

using the AI to parse the input data to extract task parameters, generate meta tags describing functions and dependencies, and construct a workflow representation of tasks, the workflow representation including operating-system-level input events and associated state transitions;

executing the workflow representation by generating the operating-system-level input events and verifying state transitions through a perception layer that captures pre-execution and post-execution states; and

logging each executed input event and corresponding state transition in a provenance module with cryptographic metadata for secure auditability;

wherein, when source code is available, the AI is trained on the codebase and associated documentation, and when source code is unavailable, the AI autonomously explores a graphical user interface by simulating user actions, capturing screenshots, and constructing workflows.

2 . The method of claim 1 , further comprising generating a dependency graph that visually represents relationships among functions, classes, modules, and external libraries, and rendering the dependency graph through an interactive interface that supports filtering by relationship type and zooming into specific nodes.

3 . The method of claim 1 , further comprising analyzing historical user interactions with the workflow representation to predict subsequent tasks and appending the predicted tasks to the workflow representation for execution.

4 . The method of claim 1 , further comprising adapting the workflow representation to include alternative operating-system-level input events in response to detecting a failure in a state transition during execution.

5 . The method of claim 1 , further comprising generating multi-modal outputs, including text, voice, graphics, and media, to provide user-facing explanations of the workflow representation and its execution results.

6 . The method of claim 1 , further comprising training a machine learning model on the meta tags and task parameters to refine the parsing of subsequent input data and enhance the accuracy of the workflow representation.

7 . The method of claim 1 , wherein the perception layer captures accessibility tree snapshots and compares hierarchical structures to verify state transitions associated with the operating-system-level input events.

8 . The method of claim 1 , wherein the provenance module stores cryptographic metadata including timestamps, unique identifiers, and hashes of pre-execution and post-execution states for auditability.

9 . The method of claim 1 , wherein the workflow representation includes conditional logic for selecting alternative state transitions in response to detecting specific error messages during execution.

10 . The method of claim 1 , wherein the workflow representation incorporates predictive task suggestions derived from analyzing patterns in historical user interactions and system logs.

11 . The method of claim 1 , wherein multiple large language models (LLMs) and generative adversarial networks (GANs) operate in parallel to parse the input data and generate outputs, and a consensus module determines the next action or response based on their collective results.

12 . The method of claim 11 , wherein the GANs generate visual aids including task flow diagrams, flowcharts, or mockups of user interfaces corresponding to the workflows.

13 . A system configured for automating code analysis, dependency mapping, and adaptive task execution with AI, comprising:

a processor;

memory coupled with the processor; and

instructions stored in the memory and executable by the processor to cause the system to:

receive input data at a server platform, the input data including a codebase and associated documentation;

using the AI to parse the input data to extract task parameters, generate meta tags describing functions and dependencies, and construct a workflow representation of tasks, the workflow representation including operating-system-level input events and associated state transitions;

execute the workflow representation by generating the operating-system-level input events and verifying state transitions through a perception layer that captures pre-execution and post-execution states; and

log each executed input event and corresponding state transition in a provenance module with cryptographic metadata for secure auditability;

wherein, when source code is available, the AI is trained on the codebase and associated documentation, and when source code is unavailable, the AI autonomously explores a graphical user interface by simulating user actions, capturing screenshots, and constructing workflows.

14 . The system of claim 13 , wherein the instructions are further executable by the processor to cause the system to:

generate a dependency graph that visually represents relationships among functions, classes, modules, and external libraries, and render the dependency graph through an interactive interface that supports filtering by relationship type and zooming into specific nodes.

15 . The system of claim 13 , wherein the instructions are further executable by the processor to cause the system to:

analyze historical user interactions with the workflow representation to predict subsequent tasks and append the predicted tasks to the workflow representation for execution.

16 . The system of claim 13 , wherein the instructions are further executable by the processor to cause the system to:

adapt the workflow representation to include alternative operating-system-level input events in response to detecting a failure in a state transition during execution.

17 . The system of claim 13 , wherein the instructions are further executable by the processor to cause the system to:

generate multi-modal outputs, including text, voice, graphics, and media, to provide user-facing explanations of the workflow representation and its execution results.

18 . The system of claim 13 , wherein the instructions are further executable by the processor to cause the system to:

train a machine learning model on the meta tags and task parameters to refine the parsing of subsequent input data and enhance the accuracy of the workflow representation.

19 . The system of claim 13 , wherein the perception layer captures accessibility tree snapshots and compares hierarchical structures to verify state transitions associated with the operating-system-level input events.

20 . The system of claim 13 , wherein the provenance module stores cryptographic metadata including timestamps, unique identifiers, and hashes of pre-execution and post-execution states for auditability.

21 . The system of claim 13 , wherein the workflow representation includes conditional logic for selecting alternative state transitions in response to detecting specific error messages during execution.

22 . A non-transitory computer-readable medium storing code for automating code analysis, dependency mapping, and adaptive task execution with AI, the code comprising instructions executable by a processor to:

receive input data at a server platform, the input data including a codebase and associated documentation;

using the AI to parse the input data to extract task parameters, generate meta tags describing functions and dependencies, and construct a workflow representation of tasks, the workflow representation including operating-system-level input events and associated state transitions;

execute the workflow representation by generating the operating-system-level input events and verifying state transitions through a perception layer that captures pre-execution and post-execution states; and

log each executed input event and corresponding state transition in a provenance module with cryptographic metadata for secure auditability;

wherein, when source code is available, the AI is trained on the codebase and associated documentation, and when source code is unavailable, the AI autonomously explores a graphical user interface by simulating user actions, capturing screenshots, and constructing workflows.

23 . A system configured for generating autonomous help desk actions through machine learning and workflow automation, comprising:

a processor;

a memory coupled with the processor; and a multiplicity of instructions stored in the memory and executable by the processor to cause the system to:

receive input data at a server platform, the input data representing one or more computer issues;

using AI to parse the input data to extract task parameters and construct a workflow representation of tasks based on the task parameters, the workflow representation including operating-system-level input events and associated state transitions; execute the workflow representation by generating the operating-system-level input events and verifying state transitions through a perception layer to ensure task completion; and

log each executed input event and corresponding state transition in a provenance module with cryptographic metadata for auditability;

wherein, when source code is available, the AI is trained on the codebase and associated documentation, and when source code is unavailable, the AI autonomously explores a graphical user interface by simulating user actions, capturing screenshots, and constructing workflows.