COURSE 9: GENERATIVE AI: ELEVATE YOUR SOFTWARE DEVELOPMENT CAREER
Module 1: Generative AI and Software Development
IBM AI DEVELOPER PROFESSIONAL CERTIFICATE
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INTRODUCTION – Generative AI and Software Development
In this module, you will discover how AI influences software development. Lesson 1 focuses on leveraging AI throughout the software development lifecycle with various tools and algorithms, setting up a development environment for AI and ChatGPT, and understanding LLMs, transformers, and NLP to create a chatbot.
In Lesson 2, you’ll delve into best practices and design patterns using AI for technical assistance and software architecture. You’ll learn how AI aids in code generation, bug detection, troubleshooting, and identifying useful AI prompts for software development. This lesson will also cover using AI to generate static websites and architecture diagrams.
Learning Objectives
- Discuss Generative AI in the context of software development and use it throughout the software development lifecycle
- List the best practices of using Generative AI for design patterns and technical help
- Explain AI algorithms like LLMs and NLP and AI tools like ChatGPT
- Build a chatbot using ChatGPT
- Describe how to leverage AI for code generation, bug detection, and troubleshooting
- Generate static websites and prototypes using AI
- Summarize how to use AI for coding problems and software architecture
- Describe how to manage legacy code with AI
PRACTICE ASSIGNMENT: SOFTWARE DEVELOPMENT AND GENERATIVE AI
1. What is the software development that AI can significantly impact?
- Documentation
- Project management
- Graphic design
- Bug detection (CORRECT)
Correct: Correct! AI, particularly machine learning algorithms, aids in training models to identify common bugs and vulnerabilities in code by analyzing patterns and past bug reports, enhancing the software quality.
2. Which of the following is a crucial role of AI in the software development lifecycle (SDLC)?
- Applying exclusively to deployment and maintenance
- Solely focusing on design phase improvements
- Contributing to all phases of the SDLC (CORRECT)
- Enhancing only testing phase procedure
Correct: Correct! AI plays a role in each phase of the software development lifecycle (SDLC), aiding in requirements analysis, design recommendations, code generation, testing automation, deployment automation, and maintenance activities.
3. How can NER models be built in NLP?
- Using deep learning methods
- Using only unsupervised learning
- Using both supervised and unsupervised learning approaches (CORRECT)
- Using only supervised learning
Correct: Correct! Building NER models include both supervised and unsupervised learning approaches.
4. Which of the following is an AI-powered automation that benefits software development?
- Slowing down the processes
- Streamlined repetitive tasks (CORRECT)
- Increased manual effort
- Decreased productivity
Correct: Correct! AI automation tools streamline repetitive tasks, reduce manual effort, and enhance productivity by automating various processes in software development.
5. What does AI-assisted code generation primarily involve?
- Providing suggestions solely for code completion
- Analyzing code for potential bugs
- Predicting and generating code snippets from existing codebases (CORRECT)
- Manual coding based on design specifications
Correct: Correct! This helps to leverage patterns and structures to generate syntactically correct code aligned with project requirements.
GENERATIVE AI FOR SOFTWARE CODING AND ARCHITECTURE
1. What action supports AI-driven tools contributing to code analysis and optimization in a CI/CD pipeline?
- Providing insights into application release quality
- Predicting deployment risks
- Analyzing user feedback
- Detecting patterns and code issues (CORRECT)
Correct: Correct! AI tools employ machine learning algorithms, detect patterns and code issues, and recommend optimizations for better codebase performance and maintenance.
2. What is the role of AI in generating high-level architecture from code in software development?
- Real-time architecture updates
- Automating code completion
- Analyzing code for design patterns (CORRECT)
- Generating innovative architectures
Correct: Correct! Traditional outside-in methods can be limiting, and an alternative approach involves directly generating architecture from the codebase itself, with AI analyzing code for reliable insights.
3. How can AI-powered code completion tools enhance the coding process?
- By increasing the chances of making syntax errors
- By manually typing out entire code snippets
- By suggesting relevant code based on context (CORRECT)
- By avoiding the use of natural language processing (CORRECT)
Correct: Correct! AI-powered code completion tools suggest relevant code based on context.
Correct: Avoiding natural language processing is not a recognized role in enhancing the coding process.
4. What role do NLP techniques play in AI-powered code completion?
- Generating code based on high-level descriptions
- Analyzing the context of the code (CORRECT)
- Summarizing technical documentation
- Analyzing user preferences
Correct: Correct! To provide accurate suggestions, NLP techniques in AI-powered code completion analyze the context of the code being written, including variable names and function signatures.
5. Which tool is mentioned explicitly for generating high-level architecture from code in web development?
- AI logo maker
- Architecture-view-nests VS Code extension (CORRECT)
- AI website generator
- A text generator
Correct: Correct! The architecture-view-nests VS Code extension is one of the tools for generating high-level architecture from code in web development.
MODULE 1: GENERATIVE AI AND SOFTWARE DEVELOPMENT
1. Which practice ensures data protection and privacy standards compliance when using generative AI?
- Ignoring privacy concerns and regulations.
- Obtaining informed consent from individuals whose data is used in training or generating content. (CORRECT)
- Incorporating techniques that increase biases in generated outputs.
- Striving for interpretability and explainability in generative AI systems.
Correct: Correct! Individuals can understand and agree to use their data in generative AI systems by obtaining informed consent.
2. What is one concern associated with generative AI models in software development?
- The ability to create synthetic vocal recordings
- The requirement to obtain informed consent from users.
- The potential for spreading false information (CORRECT)
- The need for extensive training data
Correct: Correct! Generative AI models can be programmed to generate text or content that resembles human-generated content.
3. What does GPT stand for in the context of Large Language Models (LLMs)?
- Graphical Processing Token
- Generative Pretrained Transformer (CORRECT)
- General Processing Task
- Google’s Programming Technique
Correct: Correct! GPT stands for Generative Pretrained Transformer and is one of the most widely known Large Language Models for its language-understanding capabilities.
4. How do Large Language Models (LLMs) contribute to code quality in software development?
- Enhancing user interfaces
- Automating testing and quality assurance (CORRECT)
- Optimizing hardware resources
- Creating complex algorithms
Correct: Correct! LLMs automate testing by creating test cases and validating code against predefined standards, ensuring software quality.
5. How does AI contribute to real-time architecture updates in software development projects?
- Quantum computing integration
- Automating code review (CORRECT)
- AI as a service
- Providing insights into trade-offs
Correct: Correct! AI can automate code review for higher code quality and faster development cycles.
6. Which of the following is a future trend in software development that involves making AI systems understandable by humans?
- Rise of chatbots
- AI-powered development
- Explainable AI (CORRECT)
- Quantum computing integration
Correct: Correct! Explainable AI initiative will focus on making AI systems easily understandable by humans to enhance transparency and accountability.
7. In the context of web development, AI text generator produces _________________.
- Professional logos
- HTML and CSS codes
- SEO measures
- Aesthetically pleasing content (CORRECT)
Correct: Correct! As mentioned in the range, the text generator produces top-notch content, such as product descriptions, landing pages, and blog posts.
8. What advantages does AI offer in terms of design standards for websites?
- Detects bugs
- Analyzes patterns (CORRECT)
- Includes images
- Incorporates social media
Correct: Correct! AI ensures uniform design standards across website segments by analyzing patterns and adhering to predefined design principles, promoting consistency in the design.
9. What is a potential benefit of using AI-powered bug detection tools in coding?
- Detecting potential errors or bugs efficiently (CORRECT)
- Making debugging processes more time-consuming
- Increasing the chances of syntax errors
- Ignoring errors in the code
Correct: Correct! AI-powered bug detection tools can detect potential errors or bugs efficiently, saving valuable time during debugging.
10. Which of the following is a linguistic concept forming the foundation of NLP?
- Thermodynamics
- Patterns
- Decision trees
- Syntax (CORRECT)
Correct: Correct! Linguistic concepts forming the foundation of NLP include morphology, syntax, and semantics.
CONCLUSION – Generative AI and Software Development
In conclusion, this module equips you with a comprehensive understanding of how AI can transform software development. From leveraging AI tools and algorithms in the development lifecycle to creating chatbots with LLMs, transformers, and NLP, you will gain practical skills and insights.
Additionally, you’ll explore best practices, design patterns, and how AI can enhance code generation, bug detection, and troubleshooting. By the end of this module, you’ll be well-versed in using AI to generate static websites and architecture diagrams, positioning you to effectively integrate AI into your software development processes.
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