Generative AI for Software Development : Code Generation, Error Detection, Software Testing.

Format:

Book

Author/Creator:

Kadry, Seifedine, 1977-

Contributor:

S., Balasubramaniam.

Language:

English

Physical Description:

1 online resource (330 pages)

Edition:

1st ed.

Place of Publication:

Berlin/Boston : Walter de Gruyter GmbH, 2025.

Summary:

As the field of Artificial Intelligence continues to evolve, Generative AI has emerged as a powerful tool in the realm of software development.AI models capable of generating high-quality, human-like code can significantly accelerate the software development process, assist in troubleshooting, and even automate tasks traditionally performed by.

Contents:

Intro

Preface

Contents

About the Editors

List of Contributors

1 Introduction to Generative AI in Software Development

1.1 Introduction to Generative AI in Software Development

1.2 Core Technologies Behind GenAI in Software Development

1.2.1 Deep Learning

1.2.2 Transformers

1.2.2.1 Pivotal Transformer Models in GenAI

1.2.3 Generative Adversarial Networks (GANs)

1.2.3.1 Application in Software Development

1.2.4 Variational Autoencoders

1.2.4.1 Areas of Application in Software Engineering

1.2.5 Reinforcement Learning

1.2.5.1 Applications in Software Development

1.2.6 Natural Language Processing

1.2.6.1 Software Development Applications

1.2.7 Neural Architecture Search

1.2.7.1 Usage in Software Engineering

1.2.8 Pretrained Language Models

1.2.8.1 Uses in Software Development

1.2.8.2 GenAI in Software Development [Core Technologies]

1.3 Applications of GenAI in Software Development

1.3.1 Code Generation and Autocompletion

1.3.2 Automated Bug Detection and Debugging

1.3.2.1 Benefits of Using Such Generative Models

1.3.3 Automated Testing

1.3.4 Code Refactoring and Optimization

1.3.5 Natural Language to Code Translation

1.3.6 Documentation Generation

1.3.7 Intelligent Code Review

1.3.8 Chatbots and Virtual Assistants for Development Support

1.4 Benefits of GenAI in Software Development

1.4.1 Increased Productivity and Efficiency

1.4.2 Improved Code Quality

1.4.3 Faster Testing and Quality Assurance

1.4.4 Enhanced Collaboration and Communication

1.4.5 Cost Reduction

1.4.6 Better Decision-Making and Optimization

1.4.7 Scalability and Flexibility

1.4.8 Security and Risk Mitigation

1.4.9 Improved Developer Experience

1.5 Challenges and Limitations

1.5.1 Quality and Accuracy of Generated Code.

1.5.2 Dependence on Training Data

1.5.3 Lack of Creativity and Critical Thinking

1.5.4 Security Risks

1.5.5 Maintenance and Updating of AI Model

1.5.6 Resistance to Adoption

1.6 Case Studies and Industry Applications

1.6.1 GitHub Copilot by GitHub and OpenAI

1.6.2 Tabnine

1.6.3 DeepCode (Acquired by Snyk)

1.6.4 Facebook's Aroma Tool

1.6.5 IntelliCode by Microsoft

1.6.6 Amazon Code Whisperer

1.6.7 AI-Powered Testing Tools (e.g., Test.ai)

1.6.8 CureMetrix

1.7 The Future of GenAI in Software Development

1.7.1 Automated Code Generation

1.7.2 Enhanced Debugging and Testing

1.7.3 Natural Language Interfaces for Development

1.7.4 AI-Assisted Architecture Design

1.7.5 Continuous Learning and Adaptation

1.7.6 Ethical and Responsible AI Integration

1.7.7 Integration with DevOps and CI/CD Pipelines

1.8 Conclusion

References

2 The Rise of Generative Artificial Intelligence in Software Development

2.1 Introduction

2.2 Foundations of Artificial Intelligence

2.2.1 Definition and Types of Artificial Intelligence

2.2.2 Machine Learning Versus Deep Learning

2.3 Generative Artificial Intelligence in Software Development

2.3.1 Overview and Applications

2.3.2 Benefits and Challenges

2.4 Case Studies

2.4.1 Industry Examples

2.4.2 Research and Development Projects

2.5 Ethical and Legal Implications

2.5.1 Bias and Fairness

2.5.2 Privacy and Data Security

2.6 Future Trends and Innovations

2.6.1 Advancements in GAI

2.6.2 Impact on the Software Development Industry

2.7 Summary of the Chapter

2.8 Conclusion

3 How Generative AI Models Work: Behind the Code

3.1 Introduction

3.2 Foundations of Generative AI Models

3.2.1 Neural Networks: From RNNs to Transformers

3.2.1.1 Recurrent Neural Networks (RNNs).

3.2.1.2 Long Short-Term Memory Networks (LSTMs)

3.2.1.3 Transformers

3.3 Attention Mechanisms and Self-Attention

3.4 Generative AI models

3.4.1 Encoder-Decoder Models

3.4.2 Encoder-Only Models

3.4.3 Decoder-Only Models

3.5 Training Generative AI Models

3.5.1 Pretraining

3.5.2 Fine-Tuning

3.6 Transfer Learning

3.7 Reinforcement Learning from Human Feedback (RLHF)

3.8 Tokenization and Embeddings

3.8.1 Types of Tokenization

3.8.2 Embedding Representations

3.8.3 Example: Tokenization and Embeddings

3.9 Mechanisms of Code Generation

3.9.1 Decoding Strategies

3.9.2 Output Control

3.9.3 Prompt engineering techniques

3.10 Optimization and Deployment of Generative AI Models

3.10.1 Performance Optimization

3.10.2 Deployment Strategies

3.11 Conclusions and Future Directions

4 Generative AI for Debugging and Error Detection

4.1 Background

4.2 Generative AI Practices for Debugging

4.3 Automatic Bug Discovery Tools

4.4 Role of NLP in Debugging

4.5 Developer Production Augmentation

4.6 Software Resilience Enhancement

4.7 Debugging and Error Detection Case Studies

4.8 Prospects of Gen AI in Software Error Detection

4.9 Conclusion

5 Future Frontiers of Software Testing Beyond Debugging and Accuracy Automation Driven by Generative AI

5.1 Introduction to Generative AI in Software Testing

5.2 Overview of Traditional Software Testing Methods

5.2.1 Different Stages of Transformation from Traditional to Modern Testing Methods

5.2.1.1 Manual Testing Era

5.2.1.2 Introduction of Automated Testing

5.2.1.3 Shift to Agile and DevOps Testing

5.2.1.4 Adoption of Model-Based Testing

5.2.1.5 Emergence of AI-Driven Testing

5.2.1.6 Future Frontiers with Generative AI and Autonomous Testing.

5.3 The Shift Toward Generative AI-Driven Approaches

5.4 The Role of Generative AI in Error Detection

5.5 Benefits of Automation in Software Testing

5.5.1 Increased Efficiency and Speed

5.5.2 Improved Accuracy and Consistency

5.5.3 Cost-Effectiveness in the Long Run

5.5.4 Comprehensive Test Coverage

5.5.5 Early Detection of Bugs and Issues

5.5.6 Facilitates Continuous Testing and Delivery

5.5.7 Scalability and Reusability

5.6 Generative AI Tools and Frameworks for Test Automation

5.6.1 Testim

5.6.2 Mabl

5.6.3 Applitools

5.6.4 Selenium with AI Extensions

5.6.5 Perfecto

5.6.6 AI Test Generator by Tricentis

5.6.7 Microsoft Azure DevOps with AI Insights

5.6.8 Generative Adversarial Networks (GANs) for Load Testing

5.6.9 Katalon Studio

5.6.10 DeepCode

5.7 The Role of AI in Streaming Regression Testing

5.7.1 Automated Test Case Prioritization

5.7.2 Self-Healing Test Scripts

5.7.3 Enhanced Test Coverage

5.7.4 Intelligent Defect Detection

5.7.5 Performance and Scalability Testing

5.7.6 Continuous Testing in CI/CD Pipelines

5.7.7 Predictive Analytics for Failure Prevention

5.7.8 Dynamic Test Environment Management

5.7.9 Faster Feedback Loop

5.8 Software Quality Assurance with Generative AI Tools

5.9 Strategies for Successful Generative AI Integration

5.9.1 Define Specific Objectives and Use Cases

5.9.2 Assess System Compatibility and Infrastructure

5.9.3 Choose the Right Generative AI Tools and Frameworks

5.9.4 Data Preparation and Quality Assurance

5.9.5 Modular and Scalable Integration

5.9.6 Ensure Seamless User Experience

5.9.7 Implement a Feedback Loop for Continuous Improvement

5.9.8 Security and Ethical Considerations

5.9.9 Training and Skill Development

5.9.10 Monitor and Measure Performance.

5.10 Future Landscape of Software Testing

6 Generative AI-Assisted Pair Programming: A New Era of Collaboration

6.1 Introduction to Generative AI in Software Development

6.1.1 Overview of Generative AI in Programming

6.1.2 Role of AI in Augmenting Human Abilities in Software Development

6.1.2.1 Code Generation and Completion

6.1.2.2 Error Detection and Debugging

6.1.2.3 Optimization

6.1.2.4 Testing Automation

6.1.2.5 Documentation and Knowledge Sharing

6.1.2.6 Project Management

6.1.2.7 Collaboration and Skill Enhancement

6.1.3 Evolution of Pair Programming with AI as a Virtual Collaborator

6.2 Core Capabilities and Tools for AI-Assisted Pair Programming

6.2.1 Real-Time Code Generation, Error Detection, and Debugging Assistance

6.2.1.1 Real-Time Code Generation

6.2.1.2 Error Detection

6.2.1.3 Debugging Assistance

6.2.2 Code Optimization Suggestions, Automated Documentation, and Refactoring Support

6.2.2.1 Code Optimization Suggestions

6.2.2.2 Automated Documentation

6.2.2.3 Refactoring Support

6.2.3 Steps to Integrate AI into Integrated Development Environments (IDEs) and Workflows for Specific Languages and Frameworks

6.2.3.1 AI Integration in IDEs and Workflows

6.2.3.1.1 Real-Time Code Generation

6.2.3.1.2 Error Detection

6.2.3.1.3 Debugging Assistance

6.2.3.1.4 AI-Driven Testing

6.2.3.1.5 AI for documentation

6.2.3.1.6 Continuous Learning and Model Optimization

6.3 Main Uses and Advantages of AI in Pair Programming

6.3.1 Automated Code Generation

6.3.2 Real-Time Error Detection and Debugging

6.3.3 Fast Prototyping and Complex Algorithm Resolution

6.3.4 Contextual AI Have Test Cases Written, Debugged, and Troubleshot

6.3.5 Closing the Skill Gap and Supporting Learning for Junior Developers.

6.3.6 Remote and Distributed Team Benefits Including Asynchronous Collaboration.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-11-167779-6

OCLC:

1523138076