A machine learning-based device for automating software development testing

Abstract

A machine learning-based device for automating software development testing, including: a processing unit for running machine learning models; a test execution framework that communicates with the software under development; a feedback loop mechanism for refining test cases based on results; an analysis module for real-time visualization of test metrics.

Description

Technical field of the invention

[0001] The present invention relates to a machine learning-based device for automating software development testing. BACKGROUND OF THE INVENTION

[0002] The following background information relates to the present disclosure, but does not necessarily represent prior art.

[0003] Software testing is a crucial step in the software development lifecycle, ensuring that applications meet the desired functional and non-functional requirements. Manual testing is labor-intensive, time-consuming, and error-prone, while traditional automated testing methods often cannot adapt to dynamic software changes. Machine learning (ML) offers potential solutions to these challenges by enabling adaptive learning, predictive analytics, and anomaly detection.

[0004] Current solutions lack the ability to integrate predictive capabilities and dynamically adapt to evolving software frameworks and test environments. This invention addresses these limitations by integrating a machine learning framework into a dedicated hardware device to seamlessly automate software testing.

[0005] The current state of the art / existing technology has several drawbacks. Therefore, there has long been a need in art. OBJECT OF THE INVENTION

[0006] The main objective of the present invention is to provide a machine learning based device for automating software development testing.

[0007] These and other objects and features of the present invention will become apparent from the further disclosure provided in the following detailed description. SUMMARY OF THE INVENTION

[0008] Accordingly, the following invention provides a machine learning-based apparatus for automating software development testing.

[0009] The invention provides a machine learning-based device for automating testing in software development. The device consists of: A machine learning-based device for automating software development testing. Hardware components, a machine learning-based device for automating software development testing. - A central processing unit (CPU) for running machine learning models. - A graphics processing unit (GPU) to accelerate ML model training and inference. - Input / output interfaces for integration into software development environments and external tools. - A storage module for managing test data sets, models and results.

[0010] A machine learning-based tool for automating testing in software development. Software framework, a machine learning-based tool for automating testing in software development.: - A machine learning algorithm for test case generation, defect prediction, and prioritization. - A test execution module that is integrated into software under development (SUD) to perform automated tests. - An analytics dashboard to visualize test metrics and performance. - A feedback loop mechanism that allows the system to refine its ML models based on test results.

[0011] A machine learning-based tool for automating software development testing. Key features of a machine learning-based tool for automating software development testing: - Adaptive learning: Dynamically adapts test cases based on software updates. - Predictive analysis: Identifies potential errors before test execution. - Seamless integration: Interfaces to existing CI / CD pipelines for optimized workflows. - Security compliance: Ensures the secure handling of sensitive test data. DETAILED DESCRIPTION OF THE INVENTION

[0012] As used in the description herein and in the following claims, the meaning of "a," "an," and "the" includes the plural unless the context clearly dictates otherwise. As used in the description herein, the meaning of "in" also includes "in" and "on" unless the context clearly dictates otherwise.

[0013] The present invention relates to a machine learning-based device for automating testing of software development. Hardware architecture

[0014] A machine learning-based tool for automating software development testing. Processing Unita, a machine learning-based tool for automating software development testing: - Equipped with a multi-core CPU and a high-performance GPU. - Optimized for running complex ML models in real time.

[0015] A machine learning-based device for automating software development testing.StorageA machine learning-based device for automating software development testing.: - Solid-state drives (SSD) for high-speed data storage. - Includes data encryption for security.

[0016] A machine learning-based device for automating software development testing. Interfaces: A machine learning-based device for automating software development testing. - USB and Ethernet ports for connecting external devices. - API interfaces for integration with third-party tools.

[0017] A machine learning-based device for automating software development testing. Power supplyA machine learning-based device for automating software development testing.: - Energy-efficient power modules with backup functions for uninterrupted operation. Software modules

[0018] A machine learning-based tool for automating software development testing. Machine learning models: a machine learning-based tool for automating software development testing. - Supervised learning for test case generation. - Unsupervised learning for anomaly detection. - Reinforcement learning to optimize test processes.

[0019] A machine learning-based tool for automating software development testing.Test Case GeneratorA machine learning-based tool for automating software development testing.: - Generates comprehensive test cases based on historical test data. - Prioritizes test cases based on risk assessment.

[0020] A machine learning-based tool for automating testing in software development.Test Execution FrameworkA machine learning-based tool for automating testing in software development.: - Automatically executes test cases and logs the results. - Provides compatibility with various programming languages ​​and platforms.

[0021] A machine learning-based tool for automating software development testing. Dashboard Analytics, a machine learning-based tool for automating software development testing. - Real-time visualization of test progress and defect trends. - Includes customizable reports for stakeholders.

[0022] A machine learning-based tool for automating software development testing. Continuous Learning: A machine learning-based tool for automating software development testing: - Integrates test results into the training dataset for future predictions. Advantages:

[0023] A machine learning-based tool for automating testing in software development. Efficiency: A machine learning-based tool for automating testing in software development: Reduces manual intervention and saves time and resources.

[0024] A machine learning-based tool for automating testing in software development. Adaptability - A machine learning-based tool for automating testing in software development. Responds dynamically to changes in software requirements.

[0025] A machine learning-based device for automating software development testing. Accuracya machine learning-based device for automating software development testing: Improves fault detection accuracy through predictive ML models.

[0026] A machine learning-based tool for automating software development testing. Scalability: A machine learning-based tool for automating software development testing. Suitable for large software projects with complex testing requirements. Embodiments of the invention

[0027] A machine learning-based device for automating software development testing. Standalone deviceA machine learning-based device for automating software development testing: - Works independently and can be integrated into existing software testing environments.

[0028] A machine learning-based device for automating software development testing. Network configuration: A machine learning-based device for automating software development testing. - Supports multi-device collaboration for distributed testing.

[0029] A machine learning-based device for automating software development testing. Cloud-enabled version. A machine learning-based device for automating software development testing.: - Enables remote access and cloud-based processing for improved scalability. Industrial application

[0030] The invention is applicable in various industries, including IT, finance, healthcare and telecommunications, where software development and quality assurance are critical. Diploma

[0031] The described invention represents a significant advance in the field of software testing by integrating machine learning into an automated device. It eliminates the limitations of current methods and provides a robust, efficient, and adaptable solution for testing in software development.

[0032] Although various embodiments of the present disclosure have been illustrated and described herein, it is understood that the disclosure is not limited to these embodiments. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art without departing from the spirit and scope of the disclosure as described in the claims.

Claims

[1] A machine learning-based device for automating software development testing, comprising: a processing unit for executing machine learning models; a test execution framework that communicates with the software under development; a feedback loop mechanism for refining test cases based on results; an analysis module for real-time visualization of test metrics. [2] The apparatus of claim 1, wherein the machine learning models comprise supervised, unsupervised and reinforcement learning algorithms. [3] The device of claim 1, further comprising interfaces for seamless integration with CI / CD pipelines. [4] The apparatus of claim 1, further comprising secure data encryption for test data sets and results.