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Shift Left: Accelerated Software Testing-UPDATE 2024

Shift Left: Accelerated Software Testing-UPDATE 2024

Loại khoá học: Software Testing

Test Automation, Devops for Quality Engineers, Efficient Quality Processes, Advanced Software Quality, Generative AI

image
50.000 VND
26 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

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Nội dung khoá học

Đánh giá

Mô tả

Welcome to the "Shift Left: Accelerating Quality and Efficiency" course, an in-depth exploration of the fundamentals and best practices for optimizing the software development lifecycle by identifying and addressing potential issues at earlier stages. This comprehensive course aims to equip you with the knowledge, skills, and tools needed to effectively integrate the Shift Left approach into your development processes, ultimately saving time, resources, and improving the overall quality of your software products.

We will examine the importance of code quality in the Shift Left approach. You will learn how to enforce best practices, such as code reviews, static analysis, blocking PRs and test-driven development, to ensure your codebase remains maintainable, scalable, and free from defects. By embracing these practices, you can reduce the likelihood of introducing new issues and improve the overall stability of your software.

In our chapter on automation efficiency, we will focus on leveraging automation tools and strategies to optimize your development process. By automating repetitive tasks, such as testing, integration, and deployment, you can streamline your pipeline and minimize the risk of human error. We will also discuss how to select the right tools and techniques for your specific project needs, ensuring that your team can work more efficiently and effectively.

Lastly, our course will provide you with an in-depth understanding of various prevention techniques, including vulnerability scanning, threat modeling, and secure coding practices. You will learn how to proactively identify and mitigate potential security risks, ensuring that your software is not only high-performing but also secure from potential threats.

By the end of this course, you will have gained a comprehensive understanding of the Shift Left approach, empowering you to create more efficient development pipelines, reduce risk, and deliver high-quality, reliable software products that meet the ever-evolving needs of your users.


Bạn sẽ học được gì

Shift Left approach in software development

Preventive actions in applying shift left

Practical examples of shift left pipelines

Advanced quality engineering

The test pyramid

CHAT GPT - GENERATIVE AI Demo

Proper test automation techniques

Jmeter Pefromance CI/CD Pipeline with CHAT GPT

Feature Branch Testing

Yêu cầu

  • Basic testing experience
  • Basic understanding of software development
  • Basic understanding of DevOps

Nội dung khoá học

8 sections

Introduction

2 lectures
Introduction
03:01
Project used for Automation Demonstration
03:42

Introduction to Shift Left

10 lectures
Chapter Introduction
03:13
What is shift Left approach
12:25
Benefits of Shift Left
08:15
Enabler of Shift Left - Automation
02:50
Enabler of Shift Left - Bottom-Up approach
01:48
Enabler of Shift Left - Quality Policies
02:22
Enabler of Shift Left - Reutilization
02:03
Enabler of Shift Left - Built in Quality
02:13
What is Shift Right
09:24
Why is shift Left Approach so powerful?
5 questions

Test Automation in context of shift left

13 lectures
Chapter intro
04:11
The Test Pyramid
07:07
Martin Fowler's Test Pyramid
00:02
Ice Cream Cone Antipattern
04:22
Unit Tests
06:20
Integration Tests
03:33
API / Contract Tests
13:51
UI Tests
16:52
Practical Example Test Pyramid - Java
05:50
Test Pyramid Knowledge check
4 questions
Branching - Introduction
04:36
Branching - Important Metrics for Branches
07:12
Branching - Strategies and tips
10:19

DevOps for Quality Engineers

21 lectures
Chapter Intro
04:53
Devops introduction
11:36
Continuous Integration Concept
06:52
[Demo - Github] Practical Example - Blocking for poor coding practices
05:24
[Demo - Github] - Blocking for Failing Tests
07:27
[Demo - Github] - Blocking for Code Coverage
07:41
[Demo - Github] - Blocking for Vulnerabilities
07:23
Practical Example - Validation Build
05:55
Continuous Integration Quiz
3 questions
Continuous Deployment Pipe
07:39
[Demo - Github] - Continuous Deployment with automated Tests
07:36
The Nightly Build Pipe
16:43
[Demo - Github] - The nightly run Pipeline
03:29
QE/QA Pipeline Design Advice
09:29
Design a Practical Automated Testing Pipeline
09:29
Quality Policies
04:45
Knowledge Check
4 questions
Automated Test Types
11:48
Cost and Speed Comparison API vs UI Automated Tests
05:56
Case Study - API and UI Test Automation Approach
06:37
Test Automation Framework Design
04:50

Modern Approach to Testing Environments

13 lectures
Chapter Intro
05:11
Feature Environment
07:31
[Demo - Github] - Creating a feature Environment
05:30
Environments - Modern Vision
10:24
Environments - On Demand
04:29
Environments - On Demand - Why/Benefits
03:25
Environments - On Demand - How to make it work
03:44
Automated Environment Validation
21:27
GitOPS - Safe changes to Configuration
07:55
Infrastructure Tests - Scalability / HA
08:38
Shift Left on Peformance
25:36
[Demo - Github] - Blocking pipeline for performance
05:36
Quiz: Environments
4 questions

Generative AI - Use Cases for Gen AI

10 lectures
Aid in tooling and framework Assessment
11:27
Generate Cucumber Feature Files
02:08
Generate Cucumber files with project context
01:56
Generate Automated Tests with Chat GPT
02:28
Generate Automated Tests with project context
02:00
Using AI to add jobs in CI/CD pipeline
04:23
Using AI for setting up performance testing pipelines
04:17
Using AI as a way to understand code
04:20
Using AI to calculate Unit Test coverage
05:05
Using AI to investigate unit test efficiency
07:32

Creating Jmeter Automated CI/CD pipe with CHATGPT

12 lectures
Selecting Tool with AI
02:45
Create first performance Test Script
07:35
Add listener to Test Script
03:01
GEN AI to decide context or load test
05:07
Generating Random Test Data
06:28
AI FAIL - Adding Results Summary
06:30
Adding Results Summary - The Solution
02:05
Running from Command Line
04:44
Creating Container Image
04:02
Setting up GITHUB Action
11:46
Run the Test in a Pipeline
03:00
Make Results Available
03:30

Outro

1 lectures
Closing Thoughts
01:28

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