Embracing Generative AI in Software Engineering

Conventional AI concentrates on specific tasks by following predefined rules and patterns. While it excels at tasks like data analysis, pattern recognition, and predictions, it falls short in generating entirely novel content. In contrast, generative AI strives to create new data reminiscent of human-generated content. It transcends predefined rules and patterns, leveraging training data to recognize existing patterns and generate fresh ones.

What is Gen AI?

Generative AI represents a promising technology capable of producing new content, including text, images, audio, and code. Unlike traditional AI, which adheres to predefined rules and patterns, generative AI pushes boundaries by creating fresh, human-like content. This unique selling point (USP) of generative AI can be harnessed across various phases of the software development life cycle (SDLC), leading to faster product development, improved customer experiences, and enhanced employee productivity.

In this blog, I’ll delve into best practices for integrating generative AI within the SDLC. Developers who leverage tools like ChatGPT, DALL-E, and GitHub Copilot experience transformative effects on their work. For instance, in GitHub’s study, Copilot users coded up to 55 percent faster than those who didn’t use it. Today, rules-based logic and basic machine learning assist developers by rapidly predicting coding sequence.

Gen AI can revolutionize software development Life Cycle

Generative AI significantly influences the software development life cycle (SDLC). It automates tasks, enhances software quality, and accelerates development, resulting in improved software, faster time to market, and reduced development costs.

Software Development Life Cycle

During the software development life cycle (SDLC), several key phases play crucial roles. This section captures the brief overview of tasks performed as part of SDLC. In the software development life cycle, manual artifact generation occurs during tasks such as creating user stories during requirement analysis and generating test cases in the testing phase. Regarding manual artifact generation, you’re right! In some phases (like requirement analysis and testing), artifacts are created manually. However, AI tools like Copilot can assist in content creation, including user stories.

Generative AI within SDLC

Generative AI plays a significant role across various phases of the Software Development Life Cycle (SDLC). Here are some key applications

• Code Generation: Generative AI tools like GitHub Copilot, OpenAI ChatGPT, and Google Gemini assist developers by suggesting code snippets, translating code, and improving documentation.
• Testing and Quality Assurance: AI-generated test cases can enhance code coverage and identify potential bugs.
• Natural Language Processing (NLP): Generative models can summarize documentation, write API descriptions, and even generate code comments.
• Design and Creativity: AI can create UI mockups, logos, and other design elements.
• Data Augmentation: Generative models can synthesize additional training data for machine learning models

Find the tasks that can explored using Gen AI within SDLC.

Best practice to embrace Gen AI

Let’s discuss the best practices for integrating General AI within the Software Development Life Cycle (SDLC). Engineers in various roles can follow these strategies to seamlessly embrace AI and enhance their processes. For Example, Leverage GitHub Copilot:

• GitHub Copilot is an AI-powered coding assistant that can significantly improve productivity. It provides real-time code suggestions, autocompletions, and even generates entire functions or classes based on context.
• By using Copilot, engineers can collaborate more effectively, especially during pair programming sessions. It assists in writing code, catching errors, and suggesting improvements.

For example, when addressing legacy codebases, Copilot can help with compliance checks, feature enhancements, automated unit testing, and fixing crashes and memory issues.

4 step increment process to embrace Gen AI within SDLC

This section captures a method that is a result of multiple experimentation done in our lab.

An Engineer climbing a four-step ladder. A step-by-step process to embrace the Gen AI adoption with ease and valuable ROI at every step. Each step represents the different level of development challenges and solutions.

• In the initial step, developers can leverage GitHub Copilot to generate unit tests. Our observations revealed that approximately 80% of the generated unit code was utilized with minor modifications. This approach allowed us to achieve impressive code coverage ranging from 90% to 95%.
• In the second step, identified 'Generic problems,' including sub-points such as 'Security Compliance' and 'Accessibility Compliance.' Our experimentation with GitHub Copilot yielded excellent results in this area. The multiple alternative suggestions, aligned with our code, significantly accelerated our problem-solving process. As a result, it has been observed a 15% reduction in development costs. During this stage, team leveraged GitHub Copilot for suggestions, expediting implementation, test case writing, and the generation of automated test scripts to verify our implementation."

• In the next step we targeted the issues received from customers like Crash or memory leak. By using feature "Explain code", team able to understand the legacy code with rapid speed and then by exploring prompt engineering with multiple iterations able to narrow down the issues and provide fix that improves Customer satisfaction.
• In the fourth and final step, known as 'Feature Enhancements,' Gen AI has already addressed testing, development, and maintenance phases of the Software Development Life Cycle (SDLC). Now, with the team well-equipped with Gen AI tools, they can explore additional areas such as user story creation during the requirement phase and generating design and deployment artifacts.

Gen AI tools mapping SDLC

This section list few of the Gen AI tools that can be leveraged within various SDLC phases. It captures few of the content creation artifacts generated.

Techniques to leverage generative AI

To maximize the benefits of generative AI, several techniques have evolved over time, such as prompt engineering, prompt and retrieval, and fine-tuning.

• Prompt engineering is the art and science of crafting input instructions in a way that maximizes the capabilities of generative AI models A prompt is a natural language request submitted to a language model to receive a response back. Prompts can contain questions, instructions, contextual information, few-shot examples, and partial input for the model to complete or continue.

• Prompt and Retrieval - The prompt-and-retrieval approach refines results to make them much more targeted. When a large language model trains on public data, it is not specific to one domain. Therefore, it lacks insights that could come from your proprietary data or other critical data points. By providing this depth of context, the generative AI model can produce more meaningful, customized output grounded in a specific context.
• Fine Tuning - Fine-tuning in machine learning is?the process of adapting a pre-trained model for specific tasks or use cases. It has become a fundamental deep learning technique, particularly in the training process of foundation models used for generative AI.

Conclusion

Remember that while AI tools like Copilot enhance productivity, human judgment remains essential. Engineers should critically evaluate AI-generated code and adapt it to specific project needs. By combining the strengths of AI, human expertise and validation tools like Sonar or App scan, SDLC can evolve to embrace the Gen AI era effectively.

References -

https://www.gartner.com/en/topics/generative-ai

https://en.wikipedia.org/wiki/Generative_artificial_intelligence