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Impact of Generative AI in Software Development Lifecycle
Impact of Generative AI in Software Development Lifecycle

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Generative AI is getting a lot of recognition these days. It’s a type of Artificial Intelligence (AI) that can create new content, such as text, images, sounds, and code. This technology is having a major impact on the Software Development Life Cycle (SDLC), as it can automate many of the tasks that are traditionally done by humans. The figure below explains the application of Generative AI at each stage of SDLC 

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Here are some of the benefits of using generative AI and the difference that it’s creating in SDLC, 

Benefits of Deploying Generative AI in SDLC 

Product Innovation 

One of the most significant impacts of generative AI on the SDLC is the automation of repetitive tasks. For example, generative AI can be used to generate boilerplate code, write documentation, and test software. This frees up developers to focus on more creative and complex tasks, such as designing new features and solving problems. 

Improved Customer Experience  

Generative AI is also being used to improve the quality of software. For example, generative AI can be used to find and fix bugs, improve the performance of software, and make software more secure. This can lead to a better user experience and a reduction in the cost of software development. 

Faster Product Development 

In addition to automating tasks and improving quality, generative AI is also being used to accelerate the SDLC. For example, generative AI can be used to generate prototypes of new software, which can help developers to get feedback from users early in the development process. This can lead to faster time to market, and a better understanding of what users want. 

Overall, generative AI is having a major impact on the software development lifecycle. It automates tasks, improves quality, and accelerates the development process. This is leading to better software, faster time to market, and lower costs. 

Here are some specific examples of how generative AI is being used in the SDLC by top companies today. 

Top Trending Applications of Generative AI for SDLC 

Google AI has developed a generative AI tool called AutoML Tables that can automatically generate code to build machine-learning models from data tables. This tool can save developers a significant amount of time and effort, and it can help to improve the accuracy and performance of machine learning models. 

Microsoft has developed a generative AI tool called GitHub Copilot that can automatically generate code to complete tasks in a developer's IDE. This tool can help developers to be more productive and efficient, and it can help to reduce the risk of human error. 

Salesforce has developed a generative AI tool called Einstein Discovery that can automatically generate insights from data. This tool can help developers to make better decisions about product development, marketing, and sales. 

These are just a few examples of how generative AI is being used in the SDLC. As technology continues to develop, we can expect to see even more ways that generative AI can be used to improve the software development process. 

In a Nutshell 

Generative AI is a rapidly developing field that has the potential to revolutionize the SDLC. By automating repetitive tasks, improving code quality, and generating new ideas, generative AI can help developers to build better software faster. As generative AI models become more influential, they will be able to generate novel and innovative forms of content and solve some of the world's most critical problems. Digital enterprises innovate seamlessly to gain competitive advantage, generate new revenue opportunities, enhance business agility, lower operational costs, gain operational efficiency, and accelerate time to market. As generative AI technology continues to develop, more innovative and creative applications will emerge in the future.


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