SDLC Explained: How Agile & AI Boost Your Software Projects
Anywhere Club community leaders about the new video:
— Discover an easy Introduction to the Software Development Life Cycle (SDLC) in this video! Aleksandr Gushchin, a Delivery Manager for big clients, breaks down the SDLC in software engineering with a simple, clear approach. Learn the fundamentals, explore SDLC models, and uncover best practices that drive engineering excellence.
In just 40 minutes, you'll get a simplified overview—from “What is Software Development Life Cycle” to “Software Engineering Best Practices You Should Follow” and “What AI tools should Developers use?”. See how agile methods and AI help senior programmers write better code, deliver business value faster, and reduce costs. If you're looking for a straightforward guide to the software development lifecycle, this video is a must-watch!
NAVIGATION
0:00 Intro
1:19 Fundamentals of SDLC
3:01 Enhancing Efficiency in SDLC
4:27 Reducing Costs and Avoiding Waste
5:09 Importance of Market Research in SDLC
6:33 Initial Phases of SDLC
7:50 Solution Design and Custom Development
8:52 Implementation and Testing of Solutions
10:10 Ensuring High-Quality Development Standards
11:02 Effective Communication and Documentation
12:13 Managing Known and Stable Project Scopes
13:25 Application of Agile in Predictable Environments
14:19 Factory Model in Software Development
15:09 Handling Dynamic Project Environments
16:15 Strategy and Management in Unfamiliar Situations
17:09 Experimentation and Proof of Concept in SDLC
18:23 Importance of Clear Requirements and Testing
19:17 Enhancing Developer Focus and Testing
20:09 Quality Gates and Development Output
21:10 Addressing and Managing Technical Debt
22:10 Improving Performance with Quality Initiatives
23:32 Role of Unit Testing in Quality Assurance
24:24 Continuous Quality and Assurance Practices
25:24 Shift-Left Approach in Testing
26:05 Analyzing and Optimizing Delivery Pipelines
27:26 Role of AI in Streamlining SDLC Processes
28:52 Choosing the Right Management Methodology
30:10 Efficiency of Regression Testing and Automation
31:38 Agile and Continuous Delivery in Complex Projects
33:18 Advantages of Kanban and Continuous Delivery
34:26 Evaluating and Improving SDLC Processes
35:44 Reworking Tasks for Testability and Independence
37:14 Code Documentation and Traceability
38:20 Developer's Role in Business Value Delivery
39:27 Reducing Maintenance Costs through Quality Assurance
40:29 Utilizing AI for Routine Task Automation
41:37 Enhancing SDLC with Quality Gates
42:33 Implementing Advanced Security and Analysis Tools
43:48 AI Assistance in Development and Testing
45:23 Leveraging AI for Testing and Quality Improvement
47:26 Encouraging Continuous Learning and Engagement
47:26 Final words
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