Generative AI is the most trending term in the world of artificial intelligence and has gained popularity for the right reasons. It can understand and respond to human queries in natural language, generate images, and create videos based on your ideas. Interestingly, the use cases of generative AI for developers have emerged as one of the most notable factors for driving the use of generative AI.

According to McKinsey, generative AI tools have helped improve productivity in common developer tasks. For example, the time required for documentation of code functionality for maintenance has been reduced by 50%. The popularity of generative AI skyrocketed within a record-breaking time. Facebook took 10 months to garner 100 million users, while ChatGPT did it in just 5 days! Can developers capitalize on generative artificial intelligence tools to improve their productivity?

Generative AI has the potential to revolutionize software development by fuelling productivity gains alongside accelerating innovation. On the other hand, it is important to explore diverse aspects of the use of generative AI for software development to understand its true potential for developers.

Most of the assumptions around the use of generative artificial intelligence tools by software developers point to code generation. However, it is important to identify the different areas of software development where generative AI can add value. Let us learn more about the different ways in which generative AI can help software developers.

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Will Generative AI Take Away the Job of Developers?

The most prominent concern in people’s minds when it comes to AI is its impact on jobs. The impressive achievements of generative AI tools like ChatGPT have proved that they can do tasks that require human intelligence. However, it is important to understand that generative AI would not replace software developers. 

The real question should be, “How generative AI helps developers?”, when it comes to the impact of generative AI on software development. First of all, it improves productivity by supporting faster code generation. Solutions such as Github Co-Pilot and ChatGPT can improve the efficiency of software developers by offering boilerplate code.

Developers must perceive generative AI as just another technology that requires human supervision. For example, human intervention is essential for reviewing code and checking for errors. On top of it, developers must also understand that the output of generative AI can be contextually irrelevant. Therefore, generative AI would be useful for repetitive and simple tasks. It would serve as an assistant rather than replace the developer.

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How Does Generative AI Help Developers?

The value of generative AI in the domain of software development should not be measured according to its use in code generation. You can find out more about the advantages of generative AI tools for developers by exploring practical uses of generative artificial intelligence. Developers can use generative AI to refactor a piece of code into microservices to improve reusability and maintenance. In addition, generative AI tools can support the documentation of code capabilities and introduce new application functionality to enhance the customer experience. Here are the notable areas in which generative AI can enhance software development.

  • Enhancing Speed of Repetitive Tasks

Generative AI can showcase impressive performance in the management of repetitive tasks in coding, such as auto-filling standard functions. Generative AI also helps in completing statements in code according to prompts of developers. Developers also use generative artificial intelligence to document code functionality in a specific standard format. It is clearly evident that generative AI tools can save developers time that they can invest in solving complex business challenges.

  • Initiating Code Development

Beginners in the field of software development can use generative AI tools as effective resources to begin their journey. The benefits of generative AI for developer communities worldwide revolve around opening new avenues for software developers. For example, developers can enter prompts to request suggestions for code in a different window or within the integrated development environment used for developing software.

Developers who have used generative AI tools claim that the tools offer helpful and relevant recommendations for coding. Experienced developers can also use generative AI tools to escape the programmer’s block and find a seamless flow in software development.

  • Integration of New Updates to Existing Code

Software developers can also use generative AI tools to introduce updates in existing code with the effective use of prompts. The utility of generative AI for developers also emphasizes the possibility of increasing the speed of introducing updates in existing code.

For example, generative AI tools can help in adapting code from online coding libraries to improve existing code by spending less time. Developers can copy and paste the new code in a prompt and submit repetitive queries requesting the modification of the generative AI tool according to the specified criteria.

  • Improving Ability of Developers to Addressing New Challenges

Generative AI tools can help software developers save time for complex tasks. On top of it, generative AI can also help developers with many other benefits. Generative artificial intelligence can help developers learn about a new programming language, framework, or code base required to accomplish the job. The answers to “How generative AI helps developers?” also point to the ways in which generative AI can help developers face new challenges.

Generative artificial intelligence tools can offer any type of assistance that software developers would seek from experienced colleagues. For example, generative AI can help synthesize information and explain new concepts alongside offering step-by-step guides for frameworks. Therefore, developers are 25% more likely to complete complex development tasks within the specified time by using generative artificial intelligence tools. 

The advantages of generative AI tools are not limited to these productivity gains only. Developers also need the right tools for a better developer experience. The value of generative AI tools for developers also revolves around the assurance of overall happiness and fulfillment for developers. Generative AI tools can help in the automation of repetitive tasks that can help them focus on more critical tasks. The right generative AI tools can offer developers information at their fingertips at a faster pace.

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Where Would Software Developers Matter?

Software developers can consider the advantages of generative artificial intelligence in ensuring productivity enhancements as an important factor in embracing generative artificial intelligence right away. On the other hand, the benefits of generative AI for developer experience improvement also encourage developers to adopt generative artificial intelligence. However, it is also important to remember that generative artificial intelligence tools are productive only when skilled developers or engineers use them. Here are the prominent areas in which human expertise would be an important tool for enhancing the utility of generative AI in software development.

  • Reviewing Code for Errors and Bugs

Developers who have used generative AI tools also claimed that the tools can offer incorrect recommendations for coding. In addition, generative AI tools can also introduce errors in the code. For example, developers have pointed out that generative AI can make incorrect assumptions. Therefore, developers have to use different types of prompts to rectify the incorrect assumptions of generative artificial intelligence tools. In some cases, developers would have to guide the generative AI tools through accurate prompts for correct debugging of the code.

  • Management of Ambiguous Coding Tasks

The implications of generative AI for software development also point out that generative AI tools are ideal for responding to simple prompts. For example, they can help in optimization of a code snippet. On the other hand, generative AI cannot adapt to complex coding tasks, such as a combination of multiple frameworks with different code logic.

Developers have reported that they can get a usable solution for multifaceted requirements only by manual combination of components or breaking code into smaller segments. Manual intervention by developers is necessary in scenarios where the problem is complicated. 

  • Definition of Organizational Context

Another important aspect of generative AI tools in the domain of software development is that they cannot be aligned with specific needs of an organization or project. The applications of generative AI for developers must also consider the organizational context to achieve the best results. Contextual knowledge is important for seamless integration of software products with different applications.

It can help in addressing the performance and security needs of an organization alongside ultimately solving the needs of end-users. Developers have to use prompts to provide context to generative artificial intelligence tools. For example, they can use prompts to define the approaches for using the code, authority of users, and types of interfaces that the software would interact with. 

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How Should Businesses Look at Generative AI in Software Development?

Business owners and technology leaders also have a lot of issues to handle in terms of managing the use of generative AI in software development. Technology leaders can play a major role in driving enhancements in the domain of software development with generative AI. Here are the distinct ways in which technology leaders can enable software developers to make the most of generative artificial intelligence.

  • Training and Awareness

Developers can use generative artificial intelligence technology to their advantage to improve productivity only through professional training. The answers to “How generative AI helps developers?” would depend significantly on how well developers can understand generative artificial intelligence. In the initial stages, the training of developers must focus on best practices and hands-on exercises to use natural language prompts for generative artificial intelligence tools.

Technology leaders must conduct workshops to help developers with a comprehensive overview of risks in generative artificial intelligence. For example, developers must know about intellectual property issues and industry-specific data privacy. On top of that, developers must also learn how to review AI-supported code for design, ensure functionality, comply with coding standards, and resolve complexity. Furthermore, mentors and senior team members must also guide developers through skill development paths and help them with continuous learning.

  • Look for More than Code Generation 

The potential of generative AI tools for developers is not limited to generating new code. Generative AI can also support many other common developer tasks, such as refactoring existing code. It can help technology leaders make a massive change in digital transformation efforts, which had been sidelined due to requirement of more time and resources. For example, generative artificial intelligence tools can help developers faster refactor legacy applications. As a result, developer teams can focus on closing backlogs or improving architectural performance.

  • Planning for Skill Transition 

The continuous increase in developers’ productivity requires leaders to prepare for the transition of developers to high-value tasks. It is important to create baselines for productivity and work continuously on measuring improvement. Technology leaders and business owners must consider new ways to use additional capacity of generative AI tools. Leaders must also determine the upskilling requirements to address skill gaps. For example, leaders can encourage developers to use their talent to empower new updates or business expansion.

  • Implementation of Risk Controls

Generative AI tools have also led to the rise of new data, regulatory risks, and intellectual property. Therefore, leaders must implement new risk controls and update their governance models. The effective use of generative AI for software development is possible only through risk management.

Some of the important risks that leaders must address in the use of generative artificial intelligence to develop software include legal and regulatory changes, data privacy, and AI behavioral vulnerabilities. On top of it, leaders must also account for security vulnerabilities as well as issues related to ethics and reputation. 

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Final Words

Generative artificial intelligence has the potential to transform software development in a revolutionary manner. It can achieve benefits that are impossible to achieve with other processes and tools. Developers can use generative artificial intelligence tools to complete tasks at two times the speed of other conventional solutions. The utility of generative AI tools for developers extends beyond generating code.

On the other hand, developers can make the most of generative artificial intelligence tools only through professional training, mentoring, and guidance. Technology leaders and business owners must take the initiative to help developers make the most of generative AI tools. Furthermore, developers must also experiment with different generative AI tools and try out their distinctive functionalities. Learn more about the advantages of generative AI by diving deeper into its working mechanisms right now.

About Author

James Mitchell is a seasoned technology writer and industry expert with a passion for exploring the latest advancements in artificial intelligence, machine learning, and emerging technologies. With a knack for simplifying complex concepts, James brings a wealth of knowledge and insight to his articles, helping readers stay informed and inspired in the ever-evolving world of tech.