Description:TECHNICAL FIELD
[0001] The present disclosure relates to a field of system validation, and more particularly, relates to an agentic artificial intelligence platform based methods and systems for regulating validation operations, in a software development environment.

BACKGROUND
[0002] Software testing and quality assurance represent critical components in software testing lifecycle (STLC), ensuring that applications meet functional requirements, perform reliably, and deliver value to end users. The STLC encompasses multiple phases, including test planning, test case design, test environment setup, test execution, defect tracking, and reporting. Effective management of these phases is essential for delivering high-quality software products within project timelines and budget constraints.
[0003] Conventional approaches to software testing have evolved from manual testing processes to tool-driven execution methodologies. Traditional manual testing requires significant human effort for test case creation, execution, and result analysis. More recently, cloud-based artificial intelligence (AI)-native testing platforms have emerged that provide AI assistance for test authoring, execution, and reporting. These platforms, such as D1, operate as unified intelligence layers offering AI Agent-as-a-Service models where users invoke AI capabilities to accelerate specific testing tasks. However, these approaches fundamentally rely on user-invoked AI assistance rather than autonomous operation.
[0004] Existing approaches suffer from several limitations that impede testing efficiency and effectiveness. Current systems lack autonomous lifecycle governance, requiring manual transitions between STLC phases. The monolithic AI architecture employed by conventional platforms limits scalability and specialization of testing functions. Furthermore, existing solutions generate test artifacts primarily from prompt context or limited input sources, without performing weighted fusion of heterogeneous artifacts such as functional specification documents, workflow diagrams, regression baselines, page object structures, coverage metrics, execution history, and framework constraints. This results in generative ambiguity and reduced domain alignment in generated test cases and automation scripts.
[0005] Thus, it is desirable to develop a solution to overcome one or more above-mentioned problems/challenges.

SUMMARY
[0006] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor intended to determine the scope of the invention.
[0007] According to an embodiment of the present disclosure, a method for regulating validation operations using an agentic artificial intelligence platform in a software development environment. The method includes receiving requirement artifact data comprising user stories and associated acceptance criteria. The method includes generating a fused context output by performing a weighted contextual merging of the requirement artifact data with additional requirement related metadata, based on predefined weighting parameters. Further, the method includes generating a plurality of test cases based on the fused context output. Furthermore, the method includes generating one or more executable automation scripts based on the plurality of test cases using repository introspection. The one or more executable automation scripts are compatible with a plurality of testing frameworks. Moreover, the method includes executing the one or more executable automation scripts through integration with a delivery system, thereby regulating the validation operations.
[0008] According to another embodiment, a system for regulating validation operations using an agentic artificial intelligence platform in a software development environment. The system includes a memory and a processor operatively coupled to the memory. The processor is configured to receive requirement artifact data comprising user stories and associated acceptance criteria. Further, the processor is configured to generate a fused context output by performing a weighted contextual merging of the requirement artifact data with additional requirement related metadata, based on predefined weighting parameters. Furthermore, the processor is configured to generate a plurality of test cases based on the fused context output. Moreover, the processor is configured to generate one or more executable automation scripts based on the plurality of test cases using repository introspection. The one or more executable automation scripts are compatible with a plurality of testing frameworks. Further, the processor is configured to execute the one or more executable automation scripts through integration with a delivery system, thereby regulating the validation operations.
[0009] To further clarify the advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS
[0010] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[0011] Figure 1 illustrates a block diagram depicting an environment for regulating validation operations using an agentic artificial intelligence platform in a software development environment, in accordance with an embodiment of the present disclosure;
[0012] Figure 2 illustrates a block diagram of components of the system for regulating validation operations using the agentic artificial intelligence platform in the software development environment, in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a flowchart depicting steps for regulating validation operations using the agentic artificial intelligence platform, in accordance with an embodiment of the present disclosure; and
Figure 4 illustrates a flowchart depicting a method for regulating validation operations using the agentic artificial intelligence platform in the software development environment, in accordance with an embodiment of the present disclosure.
[0013] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF FIGURES
[0014] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
[0015] The term “some” as used herein is defined as “none, or one, or more than one, or all.” Accordingly, the terms “none,” “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” The term “some embodiments” may refer to no embodiments, to one embodiment, to several embodiments, or to all embodiments. Accordingly, the term “some embodiments” is defined as meaning “no embodiment, or one embodiment, or more than one embodiment, or all embodiments.”
[0016] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements, and do not limit, restrict, or reduce the spirit and scope of the claims or their equivalents.
[0017] More specifically, any terms used herein such as but not limited to “includes,” “comprises,” “has,” “consists,” and grammatical variants thereof do NOT specify an exact limitation or restriction and certainly do NOT exclude the possible addition of one or more features or elements, unless otherwise stated, and furthermore must NOT be taken to exclude the possible removal of one or more of the listed features and elements, unless otherwise stated with the limiting language “MUST comprise” or “NEEDS TO include.”
[0018] Unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having an ordinary skill in the art.
[0019] Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and/or elements presented in the attached claims. Some embodiments have been described for the purpose of illuminating one or more of the potential ways in which the specific features and/or elements of the attached claims fulfil the requirements of uniqueness, utility, and non-obviousness.
[0020] Use of the phrases and/or terms such as but not limited to “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “a further embodiment”, “furthermore embodiment”, “additional embodiment” or variants thereof do NOT necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments.
[0021] Although one or more features and/or elements may be described herein in the context of only a single embodiment, or alternatively in the context of more than one embodiment, or further alternatively in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any feature and/or element described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment.
[0022] Any particular and all details set forth herein are used in the context of some embodiments and therefore should NOT be necessarily taken as limiting factors to the attached claims. The attached claims and their legal equivalents can be realized in the context of embodiments other than the ones used as illustrative examples in the description below.
[0023] Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
[0024] Further, skilled artisans will appreciate that those elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.
[0025] It is an object of the invention to provide a method and a system that overcome the limitations found in prior art related to system validation.
[0026] It is an object of the invention to provide an agentic artificial intelligence platform for regulating validation operations in a software development environment that enables autonomous software testing lifecycle (STLC) completion through workflow-driven multi-agent orchestration without requiring manual lifecycle transitions.
[0027] It is another object of the invention to provide a context fusion engine that performs weighted contextual merging of requirement artifact data with additional requirement-related metadata from heterogeneous sources to reduce generative ambiguity and enhance domain alignment in test case generation.
[0028] It is another object of the invention to provide repository-topology-aware code synthesis through repository introspection to generate executable automation scripts that seamlessly integrate with pre-existing automation repositories.
[0029] Figure 1 illustrates a block diagram depicting an environment 100 for regulating validation operations using an agentic artificial intelligence platform in a software development environment, in accordance with an embodiment of the present disclosure.
[0030] Referring to Figure 1, the environment 100 illustrates an implementation of a system 104 configured for regulating validation operations using the agentic artificial intelligence platform in the software development environment. In an embodiment, the environment 100 may include a user device 102 operated by a user (not shown), the system 104 instantiated on the user device 102 and/or communicatively coupled to a server 106, and a validation operations pipeline (not shown) that produces one or more executable automation scripts. In an embodiment, the system 104 may be implemented on the user device 102, such as a computing system or a workstation, and may be communicatively coupled to the server 106 for executing one or more processing tasks.
[0031] In an embodiment, the system 104 may relate to a collection of processing modules implemented in hardware, software, firmware, or any combination thereof, and configured to perform weighted contextual merging, test case generation, repository introspection, automation script synthesis, and delivery system integration.
[0032] In one implementation, the system 104 may be deployed locally on the user device 102 such that all processing required for generating the fused context output, generating the plurality of test cases, performing repository introspection, synthesizing the executable automation scripts, and executing the automation scripts through integration with a delivery system is executed on-device. In such embodiments, the system 104 may regulate the validation operations in real time or near real time in response to requirement artifact data updates. In an alternate implementation, the system 104 may be deployed partially or entirely on the server 106 or another remote computing platform, wherein the user device 102 transmits the requirement artifact data and the additional requirement-related metadata to the server 106, and the server 106 performs the weighted contextual merging, test case generation, repository introspection, and automation script synthesis before returning the generated executable automation scripts to the user device 102 for execution through the delivery system.
[0033] In an embodiment, the system 104 may include a triage module configured to determine at least one root cause in response to encountering test run failures during execution of the one or more executable automation scripts, and a defect logging module configured to register one or more defects identified in the one or more executable automation scripts based on the at least one root cause. In implementations where the system 104 is deployed partially or entirely on the server 106, communication between the user device 102 and the server 106 may occur via one or more network interfaces (not shown). Such communication may be established using wired or wireless technologies including, by way of non-limiting examples, Wi-Fi, Bluetooth, Local Area Network (LAN), cellular communication technologies, or other communication protocols capable of supporting transmission of requirement artifact data, fused context outputs, test cases, executable automation scripts, and execution results required for regulating validation operations using the agentic artificial intelligence platform.
[0034] In an embodiment, the system 104 may be configured to receive requirement artifact data comprising user stories and associated acceptance criteria. In a non-limiting example, the requirement artifact data may be obtained from project management tools that store software development requirements. For example, the system 104 may receive Jira stories containing user stories with detailed acceptance criteria that define the expected behavior of software features to be tested.
[0035] In an embodiment, the system 104 may be configured to generate the fused context output by performing a weighted contextual merging of the requirement artifact data with additional requirement-related metadata, based on predefined weighting parameters. In a non-limiting example, the weighted contextual merging combines multiple artifact sources to create a comprehensive context for test generation. For example, the system 104 may merge Jira stories with functional specification documents, workflow diagrams represented as Power Point (PPT) journey flows, regression baseline data stored in Comma Separated Values (CSV) format, existing page object structures from the automation repository, coverage metrics, execution history, and framework constraints, assigning priority weights to each source based on domain-specific relevance scores.
[0036] In an embodiment, the system 104 may be configured to generate the plurality of test cases based on the fused context output. In a non-limiting example, the test cases are automatically generated by analyzing the fused context to identify testing scenarios. For example, the system 104 may generate functional test cases that verify expected behavior, negative test cases that validate error handling, edge test cases that test boundary conditions, and boundary test cases that verify limits of input ranges.
[0037] In an embodiment, the system 104 may be configured to generate the one or more executable automation scripts based on the plurality of test cases using repository introspection. The one or more executable automation scripts are compatible with the plurality of testing frameworks. In a non-limiting example, the repository introspection analyzes existing automation framework structure in real-time to ensure generated scripts conform to established patterns. For example, the system 104 may analyze a framework repository to identify package hierarchy, page object models, entity classes, and naming conventions, then generate automation scripts compatible with testing frameworks such as Selenium, Cypress, Appium, and Rest Assured.
[0038] In an embodiment, the system 104 may be configured to execute the one or more executable automation scripts through integration with a delivery system, thereby regulating the validation operations. In a non-limiting example, the delivery system includes a continuous integration/continuous delivery pipeline that executes the automation scripts. For example, the system 104 may integrate with Jenkins or GitLab CI to trigger automated execution of the generated scripts as part of the software delivery workflow.
[0039] In an embodiment, the system 104 may be configured to determine at least one root cause in response to encountering test run failures during execution of the one or more executable automation scripts. The system 104 may be configured to register one or more defects identified in the one or more executable automation scripts based on the at least one root cause. In a non-limiting example, the triage module analyzes test run failures to identify the underlying causes. For example, the system 104 may employ a Defect Classification Agent that analyzes failure logs, identifies root causes such as element locator failures or API response errors, and automatically creates defect entries in Jira with appropriate classification and priority.
[0040] In an embodiment, the system 104 may be configured to display, on a display unit, a real-time dashboard generated based on the execution of the one or more executable automation scripts, the dashboard comprising at least one of coverage metrics, pass-fail trends, execution status, and defect trends. In a non-limiting example, a reporting module provides visualization of testing progress and results. For example, the display unit may show a dashboard presenting test coverage percentages across functional areas, historical pass-fail trend charts, current execution status of running test suites, and defect trend analysis showing defect discovery and resolution rates over time.
[0041] In an embodiment, the system 104 may include a self-healing automation module configured to reduce flaky failures during execution of the one or more executable automation scripts. In a non-limiting example, the self-healing automation module automatically detects and recovers from transient failures caused by timing issues, element locator changes, or environmental inconsistencies. For example, the self-healing automation module may automatically update element locators when UI changes are detected, retry failed test steps with adjusted wait times, or dynamically adapt to changes in the application under test without requiring manual script maintenance.
[0042] In an embodiment, the system 104 may be configured to commit the one or more executable automation scripts to a version control system prior to executing the one or more executable automation scripts. In a non-limiting example, the system performs Git operations to store generated scripts. For example, the system 104 may create a feature branch, commit the generated automation scripts with appropriate commit messages, and push the changes to the remote repository before triggering CI execution.
[0043] In an embodiment, the additional requirement-related metadata may comprise one or more of functional specification documents, workflow diagrams, regression baseline data, existing page object structures, coverage metrics, execution history, framework constraints, and historical defect data. The metadata sources provide a comprehensive context for test generation. For example, functional specification documents such as Functional Specification Document (FSD) documentation provide detailed feature descriptions, workflow diagrams represented as PPT journey flows illustrate user interaction sequences, regression baseline CSV files contain historical test data, and existing page object structures define reusable UI element mappings.
[0044] In an embodiment, the system 104 may be configured to perform the weighted contextual merging by assigning one or more priority weights to each of the requirement artifact data and the additional requirement-related metadata based on domain-specific relevance score. The weighting parameters determine the influence of each artifact source on the fused context. For example, for a user interface testing scenario, the system 104 may assign higher weights to page object structures and workflow diagrams, while for API testing scenarios, higher weights may be assigned to functional specification documents and historical defect data related to API endpoints.
[0045] In an embodiment, the plurality of test cases may comprise one or more of functional test cases, negative test cases, edge test cases, and boundary test cases. In a non-limiting example, different test case types address different aspects of software validation. For example, functional test cases verify that login functionality accepts valid credentials, negative test cases verify that invalid credentials are rejected with appropriate error messages, edge test cases verify behavior with minimum and maximum length passwords, and boundary test cases verify the exact character limits for username and password fields.
[0046] In an embodiment, the validation operations are triggered by an event. In a non-limiting example, the event comprises one or more of a requirement update event, a continuous integration event, and a webhook notification. The event-driven architecture enables autonomous lifecycle progression. For example, the system 104 may be triggered when a Jira story status changes to Ready for Testing, when a Continuous Integration/Continuous Delivery (CI/CD) pipeline completes a build stage, or when a webhook notification is received from a source control system indicating new code commits.
[0047] In an embodiment, the plurality of testing frameworks may comprise one or more of Selenium, Cypress, Appium, and Rest Assured. In a non-limiting example, the system 104 supports multiple testing frameworks to address different testing requirements. For example, Selenium may be used for web browser automation testing, Cypress for modern JavaScript application testing, Appium for mobile application testing on iOS and Android platforms, and Rest Assured for API testing and validation.
[0048] Figure 2 illustrates a block diagram of components of the system 104 for regulating validation operations using the agentic artificial intelligence platform in the software development environment, in accordance with an embodiment of the present disclosure.
[0049] In an embodiment, referring to Figures 1 and 2, the system 104 may include, but is not limited to, at least one processor 202 (alternately referred hereinafter as a processing unit 202 or the processor 202), a memory 204, one or more modules 206 (alternatively referred to as the modules), and a data unit 208. The one or more modules 206 and the memory 204 may be coupled to the processor 202. Further, the processing unit 202, the memory 204, the one or more modules 206, and the data unit 208 may be communicably coupled to each other.
[0050] In an embodiment, the processor 202 can be a single processing unit or several units, all of which could include multiple computing units. The processor 202 may be configured to execute instructions stored in the memory 204 to perform the validation operations, including weighted contextual merging, test case generation, repository introspection, automation script synthesis, and delivery system integration. The processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 202 is adapted to fetch and execute computer-readable instructions and data stored in the memory 204. At this time, one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor such as a neural processing unit (NPU). One or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning. Here, being provided through learning means that, by applying a learning technique to a plurality of learning data, a predefined operating rule or AI model of a desired characteristic is made. The learning may be performed in a device itself in which AI according to an embodiment is performed, and/or may be implemented through a separate server/system.
[0051] The AI model may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), and deep Q-networks.
[0052] The learning technique is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.
[0053] The memory 204 may include any non-transitory computer-readable medium known in the art, including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The predefined configuration may be stored in the memory 204.
[0054] The one or more modules 206, amongst other things, include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The one or more modules 206 may also be implemented as signal processor(s), state machine(s), logic circuitries, and/or any other device or component that may regulate validation operations using the agentic artificial intelligence platform in the software development environment.
[0055] Further, the one or more modules 206 can be implemented in hardware, instructions executed by the processing unit, or by a combination thereof. The processing unit can comprise a computer, a processor, a state machine, a logic array, or any other suitable device capable of processing instructions. The processing unit can be a general-purpose processor that executes instructions to cause the general-purpose processor to perform the required tasks, or the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the processor 202, via the one or more modules 206, is configured to execute machine-readable instructions (software) that perform the working of the system 104 within the scope of the present disclosure as described in forthcoming paragraphs.
[0056] In an embodiment, the data unit 208 serves, amongst other things, as a repository for storing data processed, received, and generated by the one or more modules 206.
[0057] A detailed working and explanation of the one or more modules 206 of the system 104 will be provided through various components of Figure 3 in the forthcoming paragraphs.
[0058] Figure 3 illustrates a flowchart 300 depicting steps for regulating validation operations using the agentic artificial intelligence platform, in accordance with an embodiment of the present disclosure. The processor 202 may implement an event-driven, workflow-orchestrated, multi-agent STLC management approach that autonomously coordinates lifecycle stages.
[0059] At step 302, the processor 202 may receive the requirement artifact data comprising user stories and associated acceptance criteria. The processor 202 may initiate validation operations in response to an event such as a Jira story update, which initiates a workflow trigger through the orchestration engine. In a non‑limiting example, when a user‑story status changes in Jira, the event‑driven architecture may detect the change and initiate the autonomous testing lifecycle. The orchestration engine, implemented using n8n, may manage multiple role‑specialized AI agents including a Scenario Generation Agent, a Coverage Enrichment Agent, a Framework Compliance Agent, a Execution Agent, and a Defect Classification Agent, which collaborate through the workflow orchestration layer to complete STLC phases autonomously.
[0060] In an embodiment, the processor 202 may operate the distributed cognitive architecture of the role‑specialized AI agents to provide separation of responsibilities that improves reliability, traceability, and lifecycle specialization. In a non‑limiting example, each agent may maintain a specific area of expertise and accountability within the STLC. For example, the Scenario Generation Agent may create test scenarios from requirements, the Coverage Enrichment Agent may ensure comprehensive test coverage, the Framework Compliance Agent may maintain code quality and architectural alignment, the Execution Agent may manage test execution workflows, and the Defect Classification Agent may handle failure analysis and defect categorization, thereby enabling stage‑wise autonomy across the STLC where each phase operates independently while contributing to overall autonomous lifecycle completion.
[0061] At step 304, the processor 202 may generate the fused context output by performing the weighted contextual merging of the requirement artifact data with additional requirement‑related metadata, based on predefined weighting parameters. The context fusion engine may combine heterogeneous artifact sources including Jira stories, acceptance criteria, FSD documentation, PPT journey flows representing workflow diagrams, regression baseline CSV containing historical test data, existing page object structure from the automation repository, coverage metrics indicating tested and untested areas, execution history showing previous test results, and framework constraints defining architectural requirements. The weighted contextual merging may assign priority weights to each artifact source based on domain‑specific relevance scores, reducing generative ambiguity and enhancing domain alignment in subsequent test generation.
[0062] At step 306, the processor 202 may generate the plurality of test cases based on the fused context output. For instance, the Scenario Generation Agent and the Coverage Enrichment Agent may collaborate to produce comprehensive test cases including functional test cases that verify expected behavior, negative test cases that validate error handling and invalid inputs, edge test cases that test boundary conditions and corner cases, and boundary test cases that verify input limits and constraints. The processor 202 may generate the test cases with full awareness of the fused context, ensuring alignment with user stories, acceptance criteria, and existing coverage metrics to maximize test effectiveness.
[0063] At step 308, the processor 202 may generate the one or more executable automation scripts based on the plurality of test cases using repository introspection. The executable automation scripts are compatible with the plurality of testing frameworks. The Framework Compliance Agent may perform repository introspection by analyzing the framework repository in real time to identify package hierarchy, page object models, entity classes, and naming conventions. This repository‑topology‑aware approach may ensure that the generated automation scripts preserve architectural integrity, align with existing page object models, utilize established entity classes, respect naming conventions, and maintain the overall framework structure. The processor 202 may generate the automation scripts to be compatible with testing frameworks such as Selenium for web browser automation, Cypress for JavaScript application testing, Appium for mobile application testing, and Rest Assured for API testing.
[0064] At step 310, the processor 202 may commit the one or more executable automation scripts to the version control system prior to execution. The processor 202 may perform Git operations including branch‑level code commits to store the generated automation scripts in the repository. In a non‑limiting example, the processor 202 may create appropriate feature branches, commit the generated scripts with descriptive commit messages, and push changes to the remote repository, enabling version tracking and collaborative development workflows.
[0065] In an embodiment, the processor 202 may execute the one or more executable automation scripts through integration with the delivery system, thereby regulating the validation operations. The Execution Agent may coordinate with the CI/CD system such as Jenkins or GitLab CI to trigger automated execution of the generated scripts. The integration with the delivery system may enable seamless CI execution as part of the software delivery pipeline, eliminating manual lifecycle transitions and enabling autonomous lifecycle governance.
[0066] In an embodiment, the processor 202 may determine the at least one root cause in response to encountering test‑run failures during execution of the one or more executable automation scripts, and may register one or more defects identified in the executable automation scripts based on the at least one root cause. The Defect Classification Agent may analyze test‑run failures to identify root causes such as element locator failures, API response errors, or application defects. Based on the root‑cause analysis, the processor 202 may automatically create defect entries with appropriate classification, priority, and detailed failure information. The defect logging module may integrate with Jira to enable automated test case creation and defect registration, completing the autonomous STLC lifecycle from requirement to defect without manual intervention.
[0067] In an embodiment, the processor 202 may display, on the display unit, a real‑time dashboard generated based on the execution of the one or more executable automation scripts, the dashboard comprising coverage metrics, pass‑fail trends, execution status, and defect trends. The reporting module may provide real‑time visibility into testing progress and results, enabling stakeholders to monitor the autonomous testing lifecycle and make informed decisions based on current quality metrics.
[0068] Figure 4 illustrates a flowchart depicting a method 400 for regulating validation operations using the agentic artificial intelligence platform in the software development environment, in accordance with an embodiment of the present disclosure. The method 400 may be a computer-implemented method executed, for example, by the system 104. For the sake of brevity, constructional and operational features of the system 104 that are already explained in the description of Figures 1-3 are not explained in detail in the description of Figure 4.
[0069] At step 402, the method 400 comprises receiving the requirement artifact data comprising the user stories and the associated acceptance criteria. In a non-limiting example, the requirement artifact data may be obtained from project management tools that store software development requirements in structured formats.
[0070] At step 404, the method 400 comprises generating the fused context output by performing the weighted contextual merging of the requirement artifact data with the additional requirement-related metadata, based on the predefined weighting parameters. In a non-limiting example, the weighted contextual merging combines multiple artifact sources including Jira stories, acceptance criteria, Functional Specification Document (FSD) documentation, PPT journey flows, regression baseline CSV, existing page object structures, coverage metrics, execution history, and framework constraints.
[0071] At step 406, the method 400 comprises generating the plurality of test cases based on the fused context output. In a non-limiting example, the test cases are automatically generated by analyzing the fused context to identify testing scenarios across multiple categories.
[0072] At step 408, the method 400 comprises generating the one or more executable automation scripts based on the plurality of test cases using the repository introspection. The one or more executable automation scripts are compatible with the plurality of testing frameworks. In a non-limiting example, the repository introspection analyzes the existing automation framework structure in real-time to ensure generated scripts conform to established patterns and preserve architectural integrity.
[0073] At step 410, the method 400 comprises executing the one or more executable automation scripts through integration with the delivery system, thereby regulating the validation operations. In a non-limiting example, the delivery system comprises a continuous integration/continuous delivery pipeline that executes the automation scripts as part of the software delivery workflow.
[0074] In an embodiment, the method 400 comprises determining the at least one root cause in response to encountering test run failures during execution of the one or more executable automation scripts, and registering the one or more defects identified in the one or more executable automation scripts based on the at least one root cause. In a non-limiting example, a triage module analyzes test run failures to identify the underlying causes such as element locator failures or API response errors.
[0075] In an embodiment, the method 400 comprises displaying, on the display unit, the real-time dashboard generated based on the execution of the one or more executable automation scripts, the dashboard comprising at least one of the coverage metrics, the pass-fail trends, the execution status, and the defect trends. In a non-limiting example, a reporting module provides visualization of testing progress and results.
[0076] In an embodiment, the method 400 comprises committing the one or more executable automation scripts to the version control system prior to executing the one or more executable automation scripts. In a non-limiting example, the system performs Git operations including branch-level code commits to store generated scripts.
[0077] In an embodiment, the additional requirement-related metadata comprises one or more of the functional specification documents, the workflow diagrams, the regression baseline data, the existing page object structures, the coverage metrics, the execution history, the framework constraints, and the historical defect data. In a non-limiting example, the additional requirement-related metadata sources provide comprehensive context for test generation.
[0078] In an embodiment, the method 400 comprises performing the weighted contextual merging by assigning the one or more priority weights to each of the requirement artifact data and the additional requirement-related metadata based on the domain-specific relevance score. In a non-limiting example, the weighting parameters determine the influence of each artifact source on the fused context.
[0079] In an embodiment, the plurality of test cases comprises one or more of the functional test cases, the negative test cases, the edge test cases, and the boundary test cases. In a non-limiting example, different test case types address different aspects of software validation.
[0080] In an embodiment, the method 400 comprises performing the repository introspection by analyzing the framework repository in real-time to identify at least one of the package hierarchy, the page object models, the entity classes, and the naming conventions. In a non-limiting example, the repository introspection ensures generated code aligns with existing framework architecture and preserves architectural integrity.
[0081] In an embodiment, the validation operations are triggered by the event. In a non-limiting example, the event comprises one or more of the requirements update event, the continuous integration event, and the webhook notification. The event-driven architecture enables autonomous lifecycle progression without manual intervention.
[0082] In an embodiment, the plurality of testing frameworks comprises one or more of Selenium, Cypress, Appium, and Rest Assured. In a non-limiting example, the method supports multiple testing frameworks to address different testing requirements across web, mobile, and API testing domains.
[0083] In an example scenario, the method and the system of the present disclosure may be implemented in an enterprise software development organization developing an e-commerce web application. A product owner updates a Jira story for a new checkout functionality with user stories describing an expected behavior and acceptance criteria specifying that users should be able to add items to cart, apply discount codes, select shipping options, and complete payment. The system 104 detects the Jira Story Update event and triggers the Workflow Trigger through the orchestration engine implemented using n8n. The context fusion engine performs weighted contextual merging by combining the Jira stories and acceptance criteria with FSD documentation describing the checkout workflow, PPT journey flows illustrating the user interaction sequence from cart to payment confirmation, regression baseline CSV containing historical test data for existing checkout features, existing page object structures including CartPage.java, CheckoutPage.java, and PaymentPage.java from the automation repository, coverage metrics indicating that payment gateway integration has lower test coverage, execution history showing previous failures related to discount code validation, and framework constraints specifying Selenium WebDriver patterns. The Scenario Generation Agent and the Coverage Enrichment Agent collaborate to generate a plurality of test cases including functional test cases verifying successful checkout with valid payment details, negative test cases validating error handling for invalid credit card numbers and expired discount codes, edge test cases testing checkout behavior with maximum cart quantity limits, and boundary test cases verifying character limits for shipping address fields. The Framework Compliance Agent performs repository introspection by analyzing the framework repository to identify the package hierarchy with pages, tests, and utilities directories, existing page object models following the PageFactory pattern, entity classes such as Product, Cart, and Order, and naming conventions such as CheckoutTest.java. The system 104 generates executable automation scripts compatible with Selenium that preserve the architectural integrity of the existing framework, align with the established Page Object models, and respect the naming conventions. The system 104 commits the generated automation scripts to the version control system by creating a feature branch named feature/checkout-automation, committing the scripts with descriptive commit messages, and pushing changes to the remote repository. The Execution Agent coordinates with Jenkins to trigger automated execution of the generated scripts as part of the CI/CD pipeline. During execution, the Defect Classification Agent detects test run failures related to discount code validation and determines the root cause as an API response error from the discount service. The system 104 automatically registers defects in Jira with appropriate classification indicating API integration issue, priority level, and detailed failure information including stack traces and screenshots. The reporting module displays a real-time dashboard on the display unit presenting coverage metrics showing 85% coverage for checkout functionality, pass-fail trends indicating improvement from previous test cycles, execution status showing 47 of 52 test cases passed, and defect trends showing the newly identified discount service integration defect. This example scenario demonstrates the autonomous STLC lifecycle completion from Jira Story Update through Workflow Trigger, Context Fusion, Test Case Generation, Automation Synthesis, Git Commit, CI Execution, to Defect Creation without manual intervention through workflow-driven multi-agent orchestration.
[0084] The present disclosure provides the following advantages:
a) The present disclosure provides the agentic artificial intelligence platform for regulating validation operations in a software development environment that enables autonomous STLC completion through workflow-driven multi-agent orchestration without requiring manual lifecycle transitions, thereby converting testing from tool-driven execution to workflow-governed lifecycle automation.
b) The present disclosure reduces generative ambiguity and enhances domain alignment in test case generation.
c) The present disclosure provides repository-topology-aware code synthesis through repository introspection that seamlessly integrate with pre-existing automation repositories while preserving architectural integrity, providing structural awareness of live automation frameworks, and preventing architectural corruption.
d) The present disclosure facilitates multi-agent role specialization with collaborative execution through role-specialized AI agents to complete STLC phases autonomously, thereby providing distributed cognitive architecture rather than monolithic AI.
e) The present disclosure enables event-driven autonomous STLC completion through artifact-triggered automation including Jira updates, CI events, Jenkins Triggers, and Webhooks, thereby eliminating manual lifecycle transitions and introducing cross-system orchestration intelligence.
f) The present disclosure provides automated root cause analysis and defect registration to enable autonomous defect classification and Jira integration.
g) The present disclosure facilitates real-time visibility into testing progress to monitor autonomous testing lifecycle and make informed decisions based on current quality metrics.
h) The present disclosure provides compatibility with a plurality of testing frameworks, thereby enabling comprehensive test automation across web browser automation, JavaScript application testing, mobile application testing on iOS and Android platforms, and API testing and validation.
i) The present disclosure enables seamless integration with delivery systems, thereby automating key engineering workflows.
j) The present disclosure provides vendor independence, high AI customization, strong enterprise integration, and multi-department automation capabilities.
[0085] While specific language has been used to describe the present subject matter, any limitations arising on account thereof are not intended. As would be apparent to a person in the art, various working modifications are made to the method to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements are split into multiple functional elements. Elements from one embodiment are added to another embodiment. For example, orders of processes described herein are changed and are not limited to the manner described herein. , Claims:1. A method (400) for regulating validation operations using an agentic artificial intelligence platform in a software development environment, the method comprising:
receiving (402) requirement artifact data comprising user stories and associated acceptance criteria;
generating (404) a fused context output by performing a weighted contextual merging of the requirement artifact data with additional requirement related metadata, based on predefined weighting parameters;
generating (406) a plurality of test cases based on the fused context output;
generating (408) one or more executable automation scripts based on the plurality of test cases using repository introspection, wherein the one or more executable automation scripts are compatible with a plurality of testing frameworks; and
executing (410) the one or more executable automation scripts through integration with a delivery system, thereby regulating the validation operations.

2. The method (400) as claimed in claim 1, comprising:
determining at least one root cause in response to encountering test run failures during execution of the one or more executable automation scripts; and
registering one or more defects identified in the one or more executable automation scripts based on the at least one root cause.

3. The method (400) as claimed in claim 1, comprising:
displaying, on a display unit, a real time dashboard generated based on the execution of the one or more executable automation scripts, the dashboard comprising at least one of: coverage metrics, pass fail trends, execution status, and defect trends.

4. The method (400) as claimed in claim 1, prior to executing the one or more executable automation scripts, the method (400) comprises:
committing the one or more executable automation scripts to a version control system.

5. The method (400) as claimed in claim 1, wherein the additional requirement related metadata comprises one or more of: functional specification documents, workflow diagrams, regression baseline data, existing page object structures, coverage metrics, execution history, framework constraints, and historical defect data.

6. The method (400) as claimed in claim 1, wherein the weighted contextual merging comprises assigning one or more priority weights to each of the requirement artifact data and the additional requirement related metadata based on domain specific relevance score.

7. The method (400) as claimed in claim 1, wherein the plurality of test cases comprises one or more of: functional test cases, negative test cases, edge test cases, and boundary test cases.

8. The method (400) as claimed in claim 1, wherein the repository introspection comprising:
analyzing a framework repository in real-time to identify at least one of: package hierarchy, page object models, entity classes, and naming conventions.

9. The method (400) as claimed in claim 1, wherein the validation operations is triggered by an event, wherein the event comprises one or more of: a requirement update event, a continuous integration event, and a webhook notification.

10. The method (400) as claimed in claim 1, wherein the plurality of testing frameworks comprises one or more of: Selenium, Cypress, Appium, and Rest Assured.

11. A system (104) for regulating validation operations using an agentic artificial intelligence platform in a software development environment, the system (104) comprising:
a memory (204);
a processor (202) operatively coupled to the memory (204), wherein the processor (202) is configured to:
receive requirement artifact data comprising user stories and associated acceptance criteria;
generate a fused context output by performing a weighted contextual merging of the requirement artifact data with additional requirement related metadata, based on predefined weighting parameters;
generate a plurality of test cases based on the fused context output;
generate one or more executable automation scripts based on the plurality of test cases using repository introspection, wherein the one or more executable automation scripts are compatible with a plurality of testing frameworks; and
execute the one or more executable automation scripts through integration with a delivery system, thereby regulating the validation operations.

12. The system (104) as claimed in claim 11, wherein the processor (202) is configured to:
determine at least one root cause in response to encountering test run failures during execution of the one or more executable automation scripts; and
register one or more defects identified in the one or more executable automation scripts based on the at least one root cause.

13. The system (104) as claimed in claim 11, wherein the processor (202) is configured to:
display, on a display unit, a real time dashboard generated based on the execution of the one or more executable automation scripts, the dashboard comprising at least one of: coverage metrics, pass fail trends, execution status, and defect trends.

14. The system (104) as claimed in claim 11, wherein prior to executing the one or more executable automation scripts, the processor (202) is configured to:
commit the one or more executable automation scripts to a version control system.

15. The system (104) as claimed in claim 11, wherein the additional requirement related metadata comprises one or more of: functional specification documents, workflow diagrams, regression baseline data, existing page object structures, coverage metrics, execution history, framework constraints, and historical defect data.

16. The system (104) as claimed in claim 11, wherein to perform the weighted contextual merging, the processor (202) is configured to:
assign one or more priority weights to each of the requirement artifact data and the additional requirement related metadata based on domain specific relevance score.

17. The system (104) as claimed in claim 11, wherein the plurality of test cases comprises one or more of: functional test cases, negative test cases, edge test cases, and boundary test cases.

18. The system (104) as claimed in claim 11, wherein for the repository introspection, the processor (202) is configured to:
analyze a framework repository in real-time to identify at least one of: package hierarchy, page object models, entity classes, and naming conventions.

19. The system (104) as claimed in claim 11, wherein the validation operations is triggered by an event, wherein the event comprises one or more of: a requirement update event, a continuous integration event, and a webhook notification.

20. The system (104) as claimed in claim 11, wherein the plurality of testing frameworks comprises one or more of: Selenium, Cypress, Appium, and Rest Assured.