An AI-powered system for the automated creation of test scripts

Abstract

An AI-supported system for the automatic generation of test scripts, consisting of: an AI-powered test script generator that analyzes software requirements using NLP and ML to create test scripts; a self-healing test execution engine that dynamically updates scripts in response to software changes; a CI / CD integration layer for seamless automated testing in DevOps pipelines; a predictive analysis module that detects errors and optimizes test cases; a reporting dashboard that provides real-time insights into the tests.

Description

[0001] The present invention relates to a field of software engineering and quality assurance, in particular to an AI-supported system for automatic test script generation.

[0002] Software testing is a critical phase in the software development life cycle (SDLC), ensuring functionality, performance, and security. Traditionally, test scripts are created manually, requiring significant time, expertise, and maintenance. With the increasing complexity of software applications, manual test creation becomes inefficient, leading to higher costs, delayed releases, and potential defects. Test automation has improved efficiency, but existing methods still require human intervention to create and update scripts, limiting scalability and adaptability.

[0003] To address these challenges, AI-based solutions have emerged that leverage natural language processing (NLP), machine learning (ML), and large language models (LLMs) to automate test script generation. By analyzing requirements, user stories, or existing test cases, AI can dynamically generate, optimize, and maintain test scripts. This innovation enhances DevOps and CI / CD pipelines by reducing manual effort while improving test coverage, accuracy, and adaptability to software changes.

[0004] To solve the problem, the present invention provides an AI-based automated test script generation system.

[0005] The system automates the creation of test scripts based on software requirements, user stories and existing test cases.

[0006] The system uses natural language processing (NLP) and machine learning (ML) to analyze specifications and dynamically generate test scripts.

[0007] The system integrates into DevOps and CI / CD pipelines and ensures seamless test automation throughout the entire development cycle.

[0008] The system reduces manual effort and human intervention in creating test scripts and improves efficiency and accuracy.

[0009] The system automatically updates the test scripts to adapt to changes in the software code, thus reducing maintenance effort.

[0010] The system supports multiple programming languages ​​and frameworks, ensuring compatibility with different software environments.

[0011] The system improves test coverage and accuracy by generating comprehensive test scenarios based on AI-driven analysis.

[0012] In one embodiment, an AI-based automated test script generation system is provided. The system leverages natural language processing (NLP), machine learning (ML), and large language models (LLMs) to dynamically create, optimize, and execute test scripts. Traditional test script generation requires significant manual effort, which is time-consuming and error-prone. This system automates the process by analyzing software requirements, user stories, and code changes to generate test scripts for multiple frameworks and programming languages. It integrates seamlessly with DevOps and CI / CD pipelines, ensuring continuous testing, real-time updates, and adaptive test maintenance without human intervention.

[0013] In one embodiment, this intelligent system improves the quality, reliability, and efficiency of software by automating functional, non-functional, regression, and security tests. It supports self-healing automation, detects potential vulnerabilities, and optimizes test cases for maximum coverage. The system reduces testing costs, accelerates time to market, and provides AI-driven insights into software quality trends. Thanks to its scalability and adaptability, the system is suitable for enterprise applications, cloud environments, and diverse testing requirements, making software testing more efficient, intelligent, and automated.

[0014] The invention is explained again below with reference to the figure. It shows: Fig. : a block diagram of an AI-controlled system for the automatic generation of test scripts.

[0015] Fig.shows a block diagram of an AI-driven system for automatic test script generation. The system consists of several key components that work together to automate the creation, execution, and optimization of test scripts. The heart of the system is the AI-powered test script generator, which uses natural language processing (NLP) and machine learning (ML) to analyze software requirements, user stories, or existing test cases and dynamically generate automated test scripts. This component eliminates the need for manual script creation, reducing human effort and potential errors. Furthermore, the automated test case optimization module ensures that the generated test cases are prioritized and refined to maximize test coverage and execution efficiency, improving overall software reliability.

[0016] To maintain adaptability, the self-healing test execution engine detects errors caused by software updates, automatically corrects faulty test scripts, and ensures uninterrupted execution. The system also features a DevOps & CI / CD integration layer that enables seamless integration into continuous integration and deployment (CI / CD) pipelines and ensures that automated tests are executed continuously throughout the development lifecycle. Furthermore, the test data generation module creates realistic and diverse test data, enabling functional, performance, and security testing in various scenarios.

[0017] The system enhances predictive analytics with its defect detection and risk assessment module, which uses machine learning algorithms to identify potential vulnerabilities and edge cases before software deployment. Furthermore, multi-platform and language support ensures compatibility with various programming languages ​​and automation frameworks, including Selenium, Appium, JUnit, and TestNG. The reporting and analytics dashboard provides real-time test execution metrics, defect trends, and software quality scores to provide actionable insights. This integrated, AI-powered system streamlines software testing, reduces costs, and accelerates the software release cycle, making it an essential tool for modern DevOps and software development environments. List of reference symbols 100 systems

Claims

[1] An AI-based system for the automatic creation of test scripts, consisting of: an AI-based test script generator that analyzes software requirements using NLP and ML to create test scripts; a self-healing test execution engine that dynamically updates scripts in response to software changes; a CI / CD integration layer for seamless automated testing in DevOps pipelines; a predictive analysis module that detects errors and optimizes test cases; a reporting dashboard that provides real-time insights into testing. [2] The system of claim 1, wherein the self-healing test execution engine identifies user interface changes, API modifications, and code updates and automatically updates the affected test scripts to prevent execution errors. [3] The system of claim 1, wherein the AI-driven test script generator uses historical test data and defect trends to refine and improve test case generation to increase efficiency. [4] The system of claim 1, wherein the CI / CD integration layer triggers automated regression tests on each software build or code commit, thus ensuring continuous software validation. [5] The system of claim 1, wherein the predictive analysis and fault detection module ranks test cases based on the probability of failure and prioritizes high-risk scenarios to optimize testing effort. [6] The system of claim 1, wherein the reporting and analytics dashboard provides AI-driven recommendations for test case improvements, bug fixing, and performance optimization. [7] The system of claim 1, wherein the self-healing test execution engine uses AI-based visual recognition to adapt test scripts to dynamically changing UI elements in web and mobile applications. [8] The system of claim 1, wherein the test script generator supports multiple test environments, including web, mobile, desktop, and cloud-based applications.

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