You will detect the different bugs to ensure the quality and proper functioning of mobile applications thanks to this 100% online program” 

Machine Learning applications can be vulnerable to cyber attacks and adversaries attempting to exploit weaknesses in the models. In this sense, testing is a valuable tool to help both identify and mitigate potential threats. This is why these programs must perform robustly in a variety of situations. In this way, computer scientists will be able to verify how the models behave in different scenarios and how they handle unexpected data. However, for this, it is essential for professionals to effectively handle the most advanced testing techniques and to keep abreast of developments in this area.

In this context, TECH launches a Postgraduate certificate that will delve into the various phases of the testing life cycle for quality assurance. Designed by experts in the field, the curriculum will delve into the application of early bug detection techniques through static analysis. Students will be qualified to design test plans, applying a constant evaluation to apply the necessary adjustments. The syllabus will also analyze different strategies with mobile applications based on Artificial Intelligence components. In this way, graduates will use Machine Learning algorithms to detect problems in the performance of the programs.

This teaching also facilitates learning thanks to a 100% online modality, which allows students to access the content with a device with an Internet connection (such as a cell phone, computer or tablet) and at any time or moment of the day. In this way, they will be able to plan their schedules and evaluation chronograms individually. In addition, the resource library and case studies complement this university program, which offers a great opportunity to specialize in an area that is in great demand in the technology sector.

You will design test plans to establish solid strategies, verifying that the programs are executed efficiently”

This Postgraduate certificate in Testing in Artificial Intelligence Applications contains the most complete and up-to-date program on the market. The most important features include: 

• Development of practical cases presented by experts in Artificial Intelligence in Programming
• The graphic, schematic and eminently practical contents with which it is conceived gather scientific and practical information on those disciplines that are indispensable for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Take advantage of all the benefits of the Relearning methodology, which will allow you to organize your time and pace of study, adapting to your schedule”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

You will become an API Testing expert in just 6 weeks with this university program"

You will design test plans to establish solid strategies, verifying that programs run effectively"

This university program will immerse students in the testing life cycle, from the creation of test cases to the detection of bugs. The syllabus will also provide students with innovative strategies for manual and automated test planning. In addition, the didactic content will explore various specific testing tools for mobile platforms based on Artificial Intelligence. In addition, the didactic contents will explore various specific testing tools for mobile platforms based on Artificial Intelligence. In this way, graduates will be highly qualified to take advantage of the opportunities offered by an ever-expanding IT industry. 

You will learn through real cases and by solving complex situations in simulated learning environments” 

Module 1. AI for QA Testing

1.1. Software Testing Life Cycle

1.1.1. Description and Understanding of the Testing Life Cycle in Software Development
1.1.2. Phases of the Testing Life Cycle and its Importance in Quality Assurance
1.1.3. Integration of Artificial Intelligence in Different Stages of the Testing Life Cycle
1.1.4. Strategies for Continuous Improvement of the Testing Life Cycle using AI

1.2. Test Cases and Bug Detection with the Help of ChatGPT

1.2.1. Effective Test Case Design and Writing in the Context of QA Testing
1.2.2. Identification of Bugs and Errors during Test Case Execution
1.2.3. Application of Early Bug Detection Techniques using Static Analysis
1.2.4. Use of Artificial Intelligence Tools for the Automatic Identification of Bugs in Test Cases

1.3. Types of Testing

1.3.1. Exploration of Different Types of Testing in the QA Environment
1.3.2. Unit, Integration, Functional, and Acceptance Testing: Characteristics and Applications
1.3.3. Strategies for the Selection and Appropriate Combination of Testing Types in Projects with ChatGPT
1.3.4. Adaptation of Conventional Testing Types to Projects with ChatGPT

1.4. Creation of a Testing Plan Using ChatGPT

1.4.1. Design and Structure of a Comprehensive Testing Plan
1.4.2. Identification of Requirements and Test Scenarios in AI Projects
1.4.3. Strategies for Manual and Automated Test Planning
1.4.4. Continuous Evaluation and Adjustment of the Testing Plan as the Project Develops

1.5. AI Bug Detection and Reporting

1.5.1. Implementation of Automatic Bug Detection Techniques using Machine Learning Algorithms
1.5.2. Use of ChatGPT for Dynamic Code Analysis to Search for Possible Bugs
1.5.3. Strategies for Automatic Generation of Detailed Reports on Bugs Detected Using ChatGPT
1.5.4. Effective Collaboration between Development and QA Teams in the Management of AI-Detected Bugs

1.6. Creation of Automated Testing with AI

1.6.1. Development of Automated Test Scripts for Projects Using ChatGPT
1.6.2. Integration of AI-Based Test Automation Tools
1.6.3. Using ChatGPT for Dynamic Generation of Automated Test Cases
1.6.4. Strategies for Efficient Execution and Maintenance of Automated Test Cases in AI Projects

1.7. API Testing

1.7.1. Fundamental Concepts of API Testing and its Importance in QA
1.7.2. Development of Tests for the Verification of APIs in Environments Using ChatGPT
1.7.3. Strategies for Data and Results Validation in API Testing with ChatGPT
1.7.4. Use of Specific Tools for API Testing in Projects with Artificial Intelligence

1.8. AI Tools for Web Testing

1.8.1. Exploration of Artificial Intelligence Tools for Test Automation in Web Environments
1.8.2. Integration of Element Recognition and Visual Analysis Technologies in Web Testing
1.8.3. Strategies for Automatic Detection of Changes and Performance Problems in Web Applications Using ChatGPT
1.8.4. Evaluation of Specific Tools for Improving Efficiency in Web Testing with AI

1.9. Mobile Testing Using AI

1.9.1. Development of Testing Strategies for Mobile Applications with AI Components
1.9.2. Integration of Specific Testing Tools for AI-Based Mobile Platforms
1.9.3. Use of ChatGPT for Detecting Performance Problems in Mobile Applications
1.9.4. Strategies for the Validation of Interfaces and Specific Functions of Mobile Applications by AI

1.10. QA Tools with AI

1.10.1. Exploration of QA Tools and Platforms that Incorporate Artificial Intelligence Functionality
1.10.2. Evaluation of Tools for Efficient Test Management and Test Execution in AI Projects
1.10.3. Using ChatGPT for the Generation and Optimization of Test Cases
1.10.4. Strategies for Effective Selection and Adoption of QA Tools with AI Capabilities

A flexible university program, without fixed schedules and with content available 24 hours a day. Enroll now!”