Generative AI In Coding Market Size, Share & Trends Analysis Report By Operation (Code Generation, Code Enhancement, Language Translation, Code Reviews), By Application, By Industry Vertical, By Region, And Segment Forecasts, 2023 - 2030

Generative AI In Coding Market Size, Share & Trends Analysis Report By Operation (Code Generation, Code Enhancement, Language Translation, Code Reviews), By Application, By Industry Vertical, By Region, And Segment Forecasts, 2023 - 2030

Generative AI In Coding Market Growth & Trends

The global generative AI in coding market size is expected to reach USD 106.3 million by 2030, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 25.2% from 2023 to 2030. Generative AI's impact on coding rapidly expands as developers use advanced models and successors to streamline software development. These models generate code snippets, offer creative solutions, automate documentation, enhance code comprehension, and enable rapid prototyping. As the technology evolves, its ability to address diverse programming languages and specific domains grows, empowering developers with efficient coding assistance and accelerating the iterative development process.

The rise in the development of AI models specifically designed for code generation propels the market forward. These models might be fine-tuned on programming languages, frameworks, and software development contexts, making their suggestions even more relevant and accurate. For instance, GitHub Copilot is an AI-driven coding tool that GitHub, OpenAI, and Microsoft Corporation developed. This tool offers code suggestions in Visual Studio, Neovim, and JetBrains development environments. Its ability to suggest relevant code snippets and auto-completion can save time and effort while improving code quality and consistency.

By integrating generative AI and low-code, systems can autonomously manage tasks, reducing human involvement. Low-code software democratizes development within organizations, while generative AI enhances efficiency. This synergy could streamline app creation in low-code and no-code platforms, where AI generates code from visual input. According to Microsoft's Low-Code Signals 2023 report, 87% of innovation leaders and IT professionals anticipate improved utilization through increased AI and automation within low-code platforms.

Generative AI's application in coding is on the rise throughout North America, spanning various aspects of software development. Companies in the region are actively delving into the capabilities of generative AI to elevate their coding practices. Across the U.S. and Canada, among other places, businesses are investigating the potential of AI models like GPT-3 to aid in code generation, offer suggestions, and optimize coding processes. This technology not only automates monotonous tasks but also elevates code quality and enhances productivity for developers. In the dynamic landscape of North American software development, generative AI is a tool to innovate and streamline coding efforts.

Generative AI In Coding Market Report Highlights

• Code Generation segment dominated the market with a revenue share of 37.4% in 2022. As software development becomes more pervasive across industries, the demand for code is increasing. Generative AI assists in meeting this demand by accelerating code production.
• The North America region dominated the market with a revenue share of 29.3% in 2022. The interconnected tech ecosystem in North America encourages cross-industry collaboration and knowledge-sharing, further fueling the adoption of generative AI in coding.
• The collaboration among AI research, hardware progress, and vast datasets drives market by furnishing developers with more potent and streamlined tools. These tools simplify coding tasks, resulting in enhanced efficiency and the creation of superior software products.
• As artificial intelligence gains more recognition for its effectiveness across industries, there's a rising interest in utilizing generative AI for coding, driven by its potential to enhance coding processes. This interest stems from understanding AI's value in improving coding efficiency.

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