Description:FIELD OF THE INVENTION
The present invention relates to a system and method for evaluating the maturity of software testing processes using generative artificial intelligence. The invention in particular relates to testing maturity evaluation and scoring system using generative artificial intelligence and it focuses on the automated evaluation of testing process maturity through artificial intelligence, enabling organizations to optimize their testing strategies and align them with industry best practices.
BACKGROUND OF THE INVENTION
With the increasing complexity of software testing processes and the growing diversity of testing environments, evaluating the maturity of testing practices has become a significant challenge for organizations. Traditional methods for assessing testing maturity rely heavily on manual evaluations or static metrics, which are often subjective, limited in scope, and fail to adapt to evolving testing landscapes. These approaches are time-intensive, inconsistent, and unable to handle the complexity of modern software systems. Manual assessments often vary based on individual judgment. Static models fail to adapt to diverse data inputs or evolving testing environments. Traditional methods struggle to handle large-scale or complex testing landscapes.
Existing solutions, include models like Testing Maturity Model Integration (TMMi), that provide structured methodologies but lack the flexibility to account for dynamic testing scenarios, diverse data inputs, and the need for actionable insights. Similarly, automated tools focus on isolated metrics, overlooking the broader context of testing practices, such as architectural diagrams, test environment configurations, and execution dependencies. These limitations result in incomplete assessments, inefficiencies in resource allocation, and suboptimal testing strategies.
The demand for a comprehensive and intelligent system capable of evaluating testing maturity dynamically across various domains is growing. Such a system must integrate diverse data sources, analyze complex relationships among testing artifacts, and provide actionable insights for continuous improvement. It must also seamlessly adapt to the specific needs of different industries and testing lifecycle stages, offering scalability and accuracy in evaluation.
US8161049B2 describes a system and method for assessing the quality of software testing through automated tools that analyze specific metrics, such as test coverage and defect density. While it provides a structured approach to evaluating testing quality, the invention primarily relies on static metrics and predefined parameters, lacking the flexibility to adapt to diverse testing environments or evolving data inputs. Furthermore, it does not incorporate advanced techniques like generative artificial intelligence or dynamic feedback loops, which are crucial for continuously refining testing maturity assessments in real-world scenarios.
US8869116B2 presents a method for analyzing software testing performance by utilizing a set of predefined algorithms to evaluate testing metrics and generate reports. While this approach improves efficiency in certain aspects, it lacks the capability to process unstructured data sources such as architectural diagrams, test execution logs, or contextual dependencies. Additionally, the system does not provide mechanisms for integrating industry benchmarks or adapting to domain-specific requirements, limiting its applicability in dynamic and complex testing environments.
DEFINITIONS
"Testing Maturity" refers to the level of sophistication, reliability, and efficiency of an organization’s software testing processes. It encompasses various aspects such as test planning, execution, defect management, and overall alignment with industry best practices.
"Generative artificial intelligence" refers to advanced machine learning models, such as transformers, capable of analyzing, processing, and generating text, diagrams, and other forms of data. In this invention, generative artificial intelligence is employed to derive insights, predict outcomes, and refine testing maturity evaluations.
"Structured Data" refers to organized data that resides in fixed fields within records or files, such as databases, spreadsheets, or predefined templates. Examples include test case metrics, execution logs, and defect statistics.
"Unstructured Data" refers to data that lacks a predefined format, such as architectural diagrams, textual reports, images, or natural language requirements. The system processes such data using techniques like NLP (Natural Language Processing) and OCR (Optical Character Recognition).
OBJECTS OF THE INVENTION
The primary objective of the invention is to provide a testing maturity evaluation and scoring system using generative artificial intelligence.
Another objective of the invention is to provide a system that automates the ingestion and analysis of diverse data sources, including structured and unstructured testing artifacts such as test plans, code summaries, architectural diagrams, and execution logs, enabling comprehensive maturity assessments.
A further objective of the invention is to provide a system that implements a dynamic scoring module that adapts to project-specific factors, including domain requirements and testing lifecycle phases, ensuring tailored and accurate maturity evaluations.
Yet another objective of the invention is to provide a system that creates and utilizes feature engineering techniques to extract, synthesize, and correlate metrics such as test coverage, defect density, and requirement traceability, providing deep insights into testing effectiveness.
An additional objective of the invention is to provide a system that incorporate a continuous feedback mechanism to refine scoring logic and improve system performance over time, ensuring adaptability to evolving testing practices and industry benchmarks.
Another objective of the invention is to provide a system that ensures seamless integration with existing tools and frameworks, such as JIRA, GitHub, and CI/CD pipelines, to enable real-time data collection, analysis, and reporting for scalable and efficient testing maturity evaluations.
SUMMARY OF THE INVENTION
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention discloses a system for evaluating and scoring the maturity of software testing processes, addressing challenges related to manual assessments, static metrics, and scalability in dynamic testing environments. Central to the system is a data collection module, which retrieves structured and unstructured testing data from diverse sources such as test plans, execution logs, requirements, and architectural diagrams, ensuring a unified dataset for comprehensive analysis.
According to an aspect of the present invention, the system includes an intelligent data processing module that employs advanced preprocessing techniques such as natural language processing (NLP) and optical character recognition (OCR) to standardize and analyze the data. Feature engineering techniques extract actionable metrics like test coverage, defect density, and requirement traceability, forming the foundation for maturity scoring. A key feature of the system is the generative artificial intelligence engine module, which utilizes fine-tuned language models trained on extensive testing datasets to interpret data, detect patterns, and predict future outcomes. The scoring algorithm module applies weighted metrics, dynamically adapting to project-specific factors such as domain requirements and testing lifecycle phases, to generate a comprehensive testing maturity score.
According to another aspect of the present invention, the system also includes a report generation module, which provides detailed visualizations such as radar charts and trend graphs, offering actionable insights and benchmarks for improving testing processes. A feedback mechanism module ensures continuous learning and refinement of the artificial intelligence models and scoring logic, adapting to evolving industry standards and organizational practices.
According to another aspect of the present invention, the system integrates with existing tools like JIRA, GitHub, and CI/CD pipelines, enabling seamless real-time data ingestion and reporting. This invention enhances testing efficiency by automating maturity assessments, reducing subjectivity, and providing a scalable, industry-agnostic solution for optimizing software testing strategies.
DETAILED DESCRIPTION OF THE INVENTION

Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.

The present disclosure relates to a system for assessing and scoring the maturity of software testing processes using a combination of fine-tuned large language models (LLMs), generative artificial intelligence, and deterministic scoring systems. This system analyzes diverse testing data sources, calculates a dynamic maturity score, and provides actionable insights for optimizing testing strategies. By leveraging modular components, the system ensures adaptability, scalability, and seamless integration with testing tools and environments. The present system automates the ingestion of data from multiple sources such as test documentation, execution logs, and system architectures, applies feature engineering to extract relevant metrics and identify maturity indicators, utilizes generative artificial intelligence to compute and refine a testing maturity score, generates detailed reports with benchmarks, trends, and improvement strategies and continuously learns and adapts through feedback mechanisms.

According to the embodiment of the present invention, the system comprises of the following modules: data collection module, data processing module, generative artificial intelligence engine module and scoring algorithm module. Each module plays a critical role in achieving the system's objectives. The data collection module retrieves structured and unstructured testing data from sources such as test plans, test cases, use cases, execution environments, architectural diagrams, requirements documents, and code summaries. It integrates with existing tools like JIRA, GitHub, and CI/CD pipelines, ensuring real-time data synchronization. The data processing module comprises of preprocessing module that normalizes and cleans the collected data, ensuring it is consistent and ready for analysis. It also converts unstructured data (e.g., diagrams) into analyzable formats using techniques like natural language processing (NLP) and optical character recognition (OCR). The data processing module also comprises of feature Engineering that identifies key metrics such as test coverage, defect density, and requirement traceability and generates new features through AI-driven synthesis.

According to the embodiment of the present invention, the generative artificial intelligence engine module is trained on extensive datasets of testing artifacts and industry benchmarks. It uses techniques like transformers and reinforcement learning to detects patterns, predicts future testing outcomes, and provides actionable insights. The scoring algorithm module combines weighted metrics to calculate the maturity score. It uses weight assigned to the metric based on its impact; value of the metric, derived from data analysis and maximum potential score: that is the normalization factor ensuring consistency across projects. Weights are dynamically adjusted based on project domain, testing lifecycle phase, and other factors.

According to the embodiment of the invention, the system works in the following manner:
1. Data Ingestion and Validation: The data sources including structured (databases, spreadsheets) and unstructured (PDFs, diagrams) inputs are used. These data sources are validated for completeness using predefined rules (e.g., no missing fields in test plans).
2. Preprocessing: Optical character recognition is applied for image-based inputs and natural language processing is used for extracting insights from textual data.
3.Feature Extraction and Engineering: Some examples of extracted features are:
• Test Plan Analysis: Completeness, alignment with requirements.
• Test Case Quality: Coverage, execution time, reusability.
• Defect Metrics: Defect density, resolution time.
• Environment Suitability: Configuration, scalability.
This step also generates new features by correlating existing ones (e.g., defect density vs. execution time).
4. Scoring and Weighting: This step defines weights dynamically based on project domain (e.g., healthcare, finance) and testing lifecycle phase (e.g., unit testing, integration testing). It also calculate scores using weighted averages.
5.Report Generation: This step creates detailed visualizations including radar charts for metric comparison and time-series graphs for trend analysis.
6.Feedback Loop: This step compares scores with actual testing outcomes and updates weights and retrains models to improve accuracy.
The present invention transforms software testing maturity evaluation by automating data analysis, reducing subjectivity, and providing scalable solutions for diverse testing environments. It ensures organizations can optimize their testing processes, align with industry best practices, and achieve continuous improvement. The present invention describes a comprehensive system for dynamically evaluating the maturity of software testing processes using generative artificial intelligence and advanced scoring algorithms. By leveraging fine-tuned language models and incorporating a feedback mechanism for continuous learning, this invention overcomes the limitations of the prior art. It automates the analysis of structured and unstructured data, adapts to diverse domains and testing phases, and provides actionable insights and benchmarks, ensuring precise and scalable testing maturity evaluations in rapidly evolving ecosystems.
While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.
, Claims:We claim,
1. A testing maturity evaluation and scoring system using generative artificial intelligence, characterized in that
the system comprises of a data collection module that retrieves structured and unstructured testing data, a data processing module comprises of preprocessing module that normalizes and cleans the collected data and feature engineering that identifies key metrics such as test coverage, defect density, and requirement traceability and generates new features; a generative artificial intelligence engine module that is trained on extensive datasets of testing artifacts and industry benchmarks and a scoring algorithm module that combines weighted metrics to calculate the maturity score and generates the report along with feedback loop.
2. The system as claimed in claim 1, wherein the data collection module retrieves structured and unstructured testing data from sources including test plans, test cases, use cases, execution environments, architectural diagrams, requirements documents and code summaries.
3. The system as claimed in claim 1, wherein the data collection module integrates with existing tools ensuring real-time data synchronization.
4. The system as claimed in claim 1, wherein the preprocessing module also converts unstructured data into analyzable formats using techniques like natural language processing and optical character recognition such that Optical character recognition is applied for image-based inputs and natural language processing is used for extracting insights from textual data.
5. The system as claimed in claim 1, wherein the generative artificial intelligence engine module uses techniques like transformers and reinforcement learning to detects patterns, predicts future testing outcomes, and provides actionable insights.
6. The system as claimed in claim 1, wherein the scoring algorithm module uses weight assigned to the metric based on its impact; value of the metric, derived from data analysis and maximum potential score that is the normalization factor ensuring consistency across projects.
7. The system as claimed in claim 1, wherein in the feature extraction module, the extracted features include test plan analysis, test case quality, defect metrics and environment suitability and this module also generates new features by correlating existing ones.
8. The system as claimed in claim 1, wherein the scoring algorithm module defines weights dynamically based on project domain and testing lifecycle phase and also calculate scores using weighted averages.
9. The system as claimed in claim 1, wherein the report includes detailed visualizations including radar charts for metric comparison and time-series graphs for trend analysis and the feedback loop compares scores with actual testing outcomes and updates weights and retrains models to improve accuracy.