New York, June 08, 2023 (GLOBE NEWSWIRE) -- According to MarketResearch.Biz, the Generative AI in Software Development Market size is projected to surpass around USD 169.2 Billion by 2032, and it is poised to reach a registered CAGR of 21.4% from 2023 to 2032.
Generative AI in software development is the application of artificial intelligence techniques such as deep learning and machine learning to automatically generate software code and designs. The main focus of these is to support developers in different aspects of the software development process, which improves efficiency and productivity. In the traditional method software development includes manual coding which is time-consuming. Generative AI in software development controls large datasets of existing code and uses machine learning algorithms to learn patterns and structures. With this knowledge, AI models can generate new code or provide recommendations and suggestions to developers.
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Key Takeaway
- By component, in 2022, the generative AI in software development market was dominated by the services segment.
- By deployment mode, the cloud-based segment was dominated due to the rising adoption of cloud-based technologies in software development.
- By application, code generation was dominated as a prime market during the forecast period.
- In 2022, North America dominated the market with the highest revenue share of 36.6%.
- Europe is also leading to market growth of generative AI in software development, as countries like Germany, France, and others have observed an increase in the adoption of generative AI tools in different industries.
Factors affecting the growth of generative AI in software development market?
There are several factors that can have an impact on the growth of generative AI in the software development market. Some of these factors include:
- Technological Advancements: Generative AI models continue to enhance in terms of efficiency, accuracy, and capabilities. Advancements in machine learning algorithms led to an increase in the growth of the market.
- Data Availability and Quality: Generative AI models need a large amount of data to learn from. The availability of high-quality datasets plays an important role in training models.
- Research and Development Investments: In academic institutes and private companies increased investments in research and development which led to growth in generative AI in the software development market.
Market Growth
The generative AI in the software development market is highly competitive, with a large number of players operating in the market. The companies are investing largely in R&D activities and collaborations to develop innovative generative Ai solutions for the software development sector. They are also expanding their product portfolio and geographic presence through mergers, acquisitions, and partnerships. In terms of market share, IBM Corporation is a key player in the generative AI in the finance market, with a significant share of the market.
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Top Trends
Generative AI is being used to automate software testing processes. These models can generate test cases, pretend user interactions, and identify potential bugs or irregularities in software systems. This helps in enhancing testing efficiency, ensuring software quality, and reducing human effort.
Generative AI can help in identifying security weaknesses, bug detection, or areas for code optimization. By investigating codebases, these models can automatically identify potential issues, recommend fixes, or optimize existing code to increase performance, security, and reliability.
Regional Analysis
One of the largest markets for generative AI in software development is in North America. The area boasts an adequate regulatory environment, and a well-established software development ecosystem, including startups and large technology companies. The greatest market in this region. The presence of large AI research universities and institutions has raised innovation in generative AI. Companies in North America have been fast to adopt generative AI tools and frameworks to increase the software development process, generate code scraps, and automate tasks. In North America, various successful generative AI startups are developed, which are contributing to the overall growth of the market.
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Scope of the Report
| Report Attribute | Details |
| Market Value (2022) | US$ 25.4 Mn |
| Market Size (2032) | US$ 169.2 Mn |
| CAGR (from 2023 to 2032) | 21.4%from 2023 to 2032 |
| North America Revenue Share | 36.60% |
| Historic Period | 2016 to 2022 |
| Base Year | 2022 |
| Forecast Year | 2023 to 2032 |
Market Drivers
The demand for software developers far surpasses the supply resulting in a lack of skilled professionals. Generative AI can bridge this gap by automating certain development tasks, by allowing junior developers or non-professionals to quickly generate code or create solutions with slight coding knowledge. Which helps organizations cope with the shortage of skilled developers and increase the development process. This increases the growth of generative AI in the software development market.
Market Restraints
Generative AI models such as deep learning models can be highly complex and difficult to understand how the models are making predictions. The outputs of the generative AI model may be hard to explain or understand and making it challenging for developers to change, debug or maintain the generated code or designs. The efficiency of interpretability can break the trust and adoption of generative AI in certain regulated domains where an explanation is needed.
Market Opportunities
Generative AI can be used to generate user interfaces (UIs) and design elements for software applications. By learning from user preferences and existing designs. AI models can create new UI layouts, visual elements, and color schemes, which saves the designer’s effort and time. This can be mainly useful for generating design variations and prototyping.
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Report Segmentation
Component Insight
The service segment of generative AI in software development market refers to various types of services offered to organizations or individuals looking to influence generative AI technology in their software development projects. Services include consulting and advisory services, custom development services, and training and education services. Consulting and advisory services provide guidelines on incorporating generative AI into software development projects.
Deployment Mode Insight
The cloud-based segment currently holds the top spot in the generative AI in software development market by deployment mode. This is owing to the increasing adoption of cloud-based technologies in software development and the advantages of cloud-based deployments, such as flexibility, scalability & cost-effectiveness. Cloud-based deployment mode in generative AI means the practice of deploying and running generative AI applications and models on cloud infrastructure. Cloud-based deployment mode also allows software developers to access AI capabilities and resources quickly & easily, without making significant investments in infrastructure and hardware.
Application Insight
The code generation segment holds the largest revenue share of generative AI in the software development market. In code generation, generative AI automatically generates pieces of code or even completes programs based on given specifications. It can save developers time and effort by automating repetitive tasks and providing initial code structures which is helpful for developers.
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Market Segmentation
Based on Component
- Solutions
- Services
Based on Deployment Mode
- On-Premise
- Cloud-Based
Based on Application
- Code Generation
- Code Optimization
- Bug Detection
- Testing and Quality Assurance
- Other Applications
Key Regions
- North America
- The US
- Canada
- Mexico
- Western Europe
- Germany
- France
- The UK
- Spain
- Italy
- Portugal
- Ireland
- Austria
- Switzerland
- Benelux
- Nordic
- Rest of Western Europe
- Eastern Europe
- Russia
- Poland
- The Czech Republic
- Greece
- Rest of Eastern Europe
- APAC
- China
- Japan
- South Korea
- India
- Australia & New Zealand
- Indonesia
- Malaysia
- Philippines
- Singapore
- Thailand
- Vietnam
- Rest of APAC
- Latin America
- Brazil
- Colombia
- Chile
- Argentina
- Costa Rica
- Rest of Latin America
- Middle East & Africa
- Algeria
- Egypt
- Israel
- Kuwait
- Nigeria
- Saudi Arabia
- South Africa
- Turkey
- United Arab Emirates
- Rest of MEA
Competitive Landscape
Businesses in the generative AI in software development market are developing new technologies expansion strategies through investments and mergers, and acquisitions. In addition, several key players are now focusing on the expansion of new ideas to expand the technology of generative AI in Software Development.
Some of the major players include:
- IBM Corporation
- Google LLC
- Microsoft Corporation
- Oracle Corporation
- Amazon Web Services (AWS)
- CloudBees, Inc.
- Chef Software, Inc.
- Red Hat, Inc.
- Atlassian Corporation Plc
- Other Key Players
Recent Developments
- OpenAI has made significant strides forward in the field of generative AI with their GPT-3 model, released in June 2020 and used in various software development applications including code generation and natural language understanding.
- In June 2020, GitHub unveiled a feature known as "Copilot," which uses generative AI to provide developers with code suggestions and auto-completion while they write code. Copilot relies on OpenAI's Codex language model - trained over multiple publicly available code repositories - which provides code suggestions and auto-completion while the programmer writes.
- Microsoft Research has been active in exploring and creating generative AI techniques for software development. Their team has tested neural networks as bug detectors, code completion assistants and refactoring techniques; with research papers and prototypes that demonstrate their commitment to pushing forward this field.
- Intel has been exploring how generative AI can assist with code analysis and optimization, with machine learning models to identify performance bottlenecks, optimize software for specific hardware architectures, and automate code refactoring processes.
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