Manage Pair Programming with GitHub Copilot through 150 hours of the best digital learning”

Application Programming Interface Testing (API Testing) constitutes an essential part of ensuring software quality. Through these procedures, professionals verify that programs work as expected, which contributes to the overall quality of the application.  In addition, as no manual interactions are required, coverage is faster and allows experts to save both time and resources. These tools can even be performed before user interfaces are developed, so that computer scientists can detect problems and correct them at an early stage of the development process.   

In view of this, TECH is launching an innovative program that will delve into the Testing Life Cycle using AI systems. The academic itinerary will address strategies oriented to the planning of manual and automated tests, considering that their evaluation may require continuous adjustments according to the development of the projects. At the same time, the syllabus will provide students with a holistic vision in the implementation of specific algorithms to handle problems and thus enrich the products. Also, the didactic contents will promote interoperability between different languages through automatic translation, as well as the automation of routine tasks with Computational Intelligence tools.

In short, this 6-month university program will provide students with a solid theoretical and practical foundation, enabling them to apply it in real situations, thanks to the leadership and support of a distinguished faculty of experts with extensive professional experience. In this way, TECH makes available to the student the exclusive Relearning methodology, an innovative pedagogical methodology based on the reiteration of essential concepts, thus guaranteeing an effective assimilation of knowledge.  The only requirement to enter the Virtual Campus is that the student has a device with Internet access, and may use their own cell phone.

Improve test coverage by identifying critical areas using Artificial Intelligence”  

This Postgraduate diploma in Application of Artificial Intelligence Techniques in the Life Cycle of Software Projects 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 the self-assessment process can be carried out 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

You will apply the most advanced strategies for automatic detection of changes and performance issues in web applications”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this training 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 implement Clean Architecture in your software and improve communication between different teams"

Thanks to the Relearning system used by TECH you will reduce the long hours of study and memorization"

This Postgraduate diploma will provide students with a comprehensive approach to the implementation of AI techniques in software projects. The itinerary will cover everything from the configuration of the development environment to repository management. It will also highlight the integration of elements in Visual Studio Code and code optimization with ChatGPT. The materials will delve into program architecture, providing both tools and methodologies for continuous performance monitoring, and will guide experts through the Testing Life Cycle, from test case creation to bug detection. 

A complete syllabus that incorporates all the knowledge you need to take a step towards maximum IT quality”

Module 1. Software Development Productivity Improvement with AI 

1.1. Preparing a Suitable Development Environment

1.1.1. Essential Tool Selection for AI Development
1.1.2. Configuration of the Selected Tools
1.1.3. Implementation of CI/CD Pipelines Adapted to AI Projects
1.1.4. Efficient Management of Dependencies and Versions in Development Environments

1.2. Essential AI Extensions for Visual Studio Code

1.2.1. Exploring and Selecting AI Extensions for Visual Studio Code
1.2.2. Integrating Static and Dynamic Analysis Tools into the Integrated Development Environment (IDE)
1.2.3. Automation of Repetitive Tasks with Specific Extensions
1.2.4. Customization of the Development Environment to Improve Efficiency

1.3. No-Code Design of User Interfaces with AI Elements

1.3.1. No-Code Design Principles and their Application to User Interfaces
1.3.2. Incorporation of AI Elements in Visual Interface Design
1.3.3. Tools and Platforms for the No-Code Creation of Intelligent Interfaces
1.3.4. Evaluation and Continuous Improvement of No-code Interfaces with AI

1.4. Code Optimization Using ChatGPT

1.4.1. Duplicate Code Detection
1.4.2. Refactor 
1.4.3. Create Readable Code
1.4.4. Understanding What Code Does
1.4.5. Improving Variable and Function Naming
1.4.6. Creating Automatic Documentation

1.5. Repository Management with AI

1.5.1. Automation of Version Control Processes with AI Techniques
1.5.2. Conflict Detection and Automatic Resolution in Collaborative Environments
1.5.3. Predictive Analysis of Changes and Trends in Code Repositories
1.5.4. Improvements in the Organization and Categorization of Repositories using AI

1.6. Integration of AI in Database Management

1.6.1. Optimization of Queries and Performance Using AI Techniques
1.6.2. Predictive Analysis of Database Access Patterns
1.6.3. Implementation of Recommender Systems to Optimize Database Structure
1.6.4. Proactive Monitoring and Detection of Potential Database Problems

1.7. Fault Detection and Creation of Unit Tests with AI ChatGPT

1.7.1. Automatic Generation of Test Cases using AI Techniques
1.7.2. Early Detection of Vulnerabilities and Bugs using Static Analysis with AI
1.7.3. Improving Test Coverage by Identifying Critical Areas by AI

1.8. Pair Programming with GitHub Copilot

1.8.1. Integration and Effective Use of GitHub Copilot in Pair Programming Sessions
1.8.2. Integration Improvements in Communication and Collaboration among Developers with GitHub Copilot
1.8.3. Integration Strategies to Maximize the Use of GitHub Copilot-Generated Code suggestions
1.8.4. Integration Case Studies and Best Practices in AI-Assisted Pair Programming

1.9. Automatic Translation between Programming Languages Using ChatGPT

1.9.1. Specific Machine Translation Tools and Services for Programming Languages
1.9.2. Adaptation of Machine Translation Algorithms to Development Contexts
1.9.3. Improvement of Interoperability between Different Languages by Machine Translation
1.9.4. Assessment and Mitigation of Potential Challenges and Limitations in Machine Translation

1.10. Recommended AI Tools to Improve Productivity

1.10.1. Comparative Analysis of AI Tools for Software Development
1.10.2. Integration of AI Tools in Workflows
1.10.3. Automation of Routine Tasks with AI Tools
1.10.4. Evaluation and Selection of Tools Based on Project Context and Requirements

Module 2. Software Architecture with AI

2.1. Optimization and Performance Management in AI Tools with the Help of ChatGPT

2.1.1. Performance Analysis and Profiling in AI Tools
2.1.2. Algorithm Optimization Strategies and AI Models
2.1.3. Implementation of Caching and Parallelization Techniques to Improve Performance
2.1.4. Tools and Methodologies for Continuous Real-Time Performance Monitoring

2.2. Scalability in AI Applications Using ChatGPT

2.2.1. Scalable Architectures Design for AI Applications
2.2.2. Implementation of Partitioning and Load Sharing Techniques
2.2.3. Workflow and Workload Management in Scalable Systems
2.2.4. Strategies for Horizontal and Vertical Expansion in Variable Demand Environments

2.3. Maintainability of AI Applications Using ChatGPT

2.3.1. Design Principles to Facilitate Maintainability in IA Projects
2.3.2. Specific Documentation Strategies for AI Models and Algorithms
2.3.3. Implementation of Unit and Integration Tests to Facilitate Maintainability
2.3.4. Methods for Refactoring and Continuous Improvement in Systems with AI Components

2.4. Large-Scale System Design

2.4.1. Architectural Principles for Large-Scale System Design
2.4.2. Decomposition of Complex Systems into Microservices
2.4.3. Implementation of Specific Design Patterns for Distributed Systems
2.4.4. Strategies for Complexity Management in Large-Scale Architectures with AI Components

2.5. Large-Scale Data Warehousing for AI Tools

2.5.1. Selection of Scalable Data Storage Technologies
2.5.2. Design of Database Schemas for Efficient Handling of Large Data Volumes
2.5.3. Partitioning and Replication Strategies in Massive Data Storage Environments
2.5.4. Implementation of Data Management Systems to Ensure Integrity and Availability in AI Projects

2.6. Data Structures with AI Using ChatGPT

2.6.1. Adaptation of Classical Data Structures for Use with AI Algorithms
2.6.2. Design and Optimization of Specific Data Structures with ChatGPT
2.6.3. Integration of Efficient Data Structures in Data Intensive Systems
2.6.4. Strategies for Real-Time Data Manipulation and Storage in AI Data Structures 

2.7. Programming Algorithms for AI Products

2.7.1. Development and Implementation of Application-Specific Algorithms for AI Applications
2.7.2. Algorithm Selection Strategies according to Problem Type and Product Requirements
2.7.3. Adaptation of Classical Algorithms for Integration into AI Systems
2.7.4. Evaluation and Performance Comparison between Different Algorithms in Development Contexts with AI

2.8. Design Patterns for AI Development

2.8.1. Identification and Application of Common Design Patterns in Projects with AI Components
2.8.2. Development of Specific Patterns for the Integration of Models and Algorithms into Existing Systems
2.8.3. Strategies for the Implementation of Patterns to Improve Reusability and Maintainability in AI Projects
2.8.4. Case Studies and Best Practices in the Application of Design Patterns in AI Architectures

2.9. Implementation of Clean Architecture using ChatGPT

2.9.1. Fundamental Principles and Concepts of Clean Architecture
2.9.2. Adaptation of Clean Architecture to Projects with AI Components
2.9.3. Implementation of Layers and Dependencies in Systems with Clean Architecture
2.9.4. Benefits and Challenges of Implementing Clean Architecture in Software Development with AI

2.10. Secure Software Development in Web Applications with DeepCode

2.10.1. Principles of Security in the Development of Software with AI Components
2.10.2. Identification and Mitigation of Potential Vulnerabilities in AI Models and Algorithms
2.10.3. Implementation of Secure Development Practices in Web Applications with Artificial Intelligence Functionalities
2.10.4. Strategies for the Protection of Sensitive Data and Prevention of Attacks in AI Projects

Module 3. AI for QA Testing

3.1. Software Testing Life Cycle

3.1.1. Description and Understanding of the Testing Life Cycle in Software Development 
3.1.2.  Phases of the Testing Life Cycle and Its Importance in Quality Assurance
3.1.3. Integration of Artificial Intelligence in Different Stages of the Testing Life Cycle
3.1.4. Strategies for Continuous Improvement of the Testing Life Cycle using AI

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

3.2.1. Effective Test Case Design and Writing in the Context of QA Testing
3.2.2. Identification of Bugs and Errors during Test Case Execution
3.2.3. Application of Early Bug Detection Techniques Using Static Analysis
3.2.4. Use of Artificial Intelligence Tools for the Automatic Identification of Bugs in Test Cases

3.3. Types of Testing

3.3.1. Exploration of Different Types of Testing in the QA Environment
3.3.2. Unit, Integration, Functional, and Acceptance Testing: Characteristics and Applications
3.3.3. Strategies for the Selection and Appropriate Combination of Testing Types in Projects with ChatGPT
3.3.4. Adaptation of Conventional Testing Types to Projects with ChatGPT

3.4. Creation of a Testing Plan Using ChatGPT

3.4.1. Design and Structure of a Comprehensive Testing Plan
3.4.2. Identification of Requirements and Test Scenarios in AI Projects
3.4.3. Strategies for Manual and Automated Test Planning
3.4.4. Continuous Evaluation and Adjustment of the Testing Plan as the Project Develops

3.5. AI Bug Detection and Reporting

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

3.6. Creation of Automated Testing with AI

3.6.1. Development of Automated Test Scripts for Projects Using ChatGPT
3.6.2. Integration of AI-Based Test Automation Tools
3.6.3. Using ChatGPT for Dynamic Generation of Automated Test Cases
3.6.4. Strategies for Efficient Execution and Maintenance of Automated Test Cases in AI Projects

3.7. API Testing 

3.7.1. Fundamental Concepts of API Testing and Its Importance in QA
3.7.2. Development of Tests for the Verification of APIs in Environments Using ChatGPT
3.7.3. Strategies for Data and Results Validation in API Testing with ChatGPT
3.7.4. Use of Specific Tools for API Testing in Projects with Artificial Intelligence

3.8. AI Tools for Web Testing

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

3.9. Mobile Testing Using AI

3.9.1. Development of Testing Strategies for Mobile Applications with AI Components
3.9.2. Integration of Specific Testing Tools for AI-Based Mobile Platforms
3.9.3. Use of ChatGPT for Detecting Performance Problems in Mobile Applications
3.9.4. Strategies for the Validation of Interfaces and Specific Functions of Mobile Applications by AI

3.10. QA Tools with AI 

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

TECH provides you with a high-quality and flexible Postgraduate diploma. Access conveniently from your computer, mobile or tablet!"