Global Generative AI in Software Development Market, By Component (Solutions and Services) By Deployment Mode (On-Premise and Cloud-Based), By Application (Code Generation, Code Optimization, and Other ), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Global Generative AI in Software Development Market size is expected to be worth around USD 169.2 Mn by 2032 from USD 25.4 Mn in 2022, growing at a CAGR of 21.4% during the forecast period 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 time-consuming manual coding.

Generative AI in software development controls large datasets of existing code and uses machine learning algorithms to learn patterns and structures. AI models can generate new code or provide recommendations and suggestions to developers.

Generative AI in software development can help developers in tasks such as code generation or completion, suggesting function and variable names, and detecting bugs. With these tasks, generative AI in software development focuses to speed up the development process and improve the quality of software.

Driving Factors

Shortage of Skilled Developers:

The demand for software developers far surpasses the supply it resulting in a shortage of skilled professionals. Generative AI can bridge this gap by automating certain development tasks, by allowing junior developers or non-experts to quickly generate code or create solutions with slight coding knowledge. Which helps organizations cope with the shortage of skilled developers and accelerate the development process. This drives the growth of generative AI in software development market.

Advances in Machine Learning Algorithms:

The development of advanced machine learning algorithms, like deep learning and reinforcement learning, is made it possible to train models on vast amounts of data and generate highly accurate predictions. This has allowed software developers to use generative Ai for a wide range of applications, such as code generation, code debugging, and code completion.

Restraining Factors

Limited Interpretability:

Generative AI models such as deep learning models can be highly complex and difficult to interpret and understand how the models are making predictions. The outputs of a generative AI model may be difficult to explain or understand and making it challenging for developers to modify, debug or maintain the generated code or designs. The lack of interpretability can break the trust and adoption of generative AI in certain regulated domains where an explanation is essential.

Limited Control and Constraints:

Generative AI models work based on patterns and examples in the training data, and they may not always follow specific design guidelines or constraints. While generative AI can produce creative outputs and novel. It may not always align with constraints defined by developers or desired specifications. These limitations in control can be a constraint in situations where precise specifications and devotion to strategies are critical.

COVID-19 Impact Analysis

The COVID-19 pandemic has had a significant impact on the software development market, including the adoption and development of generative AI. Here are some ways in which COVID-19 has affected generative AI in software development market.

The pandemic has increased demand for automation tools, as remote work became the standard. The pandemic enhanced digital transformation efforts which, led to greater adoption of generative AI solutions to optimize the software development process. With the help of generative AI tools remote collaboration and agile development practices received a boost. However, there were negative effects as well. Due to budget constraints and economic uncertainties, some organizations have to scale back on technology investments, including generative AI.

The COVID-19 pandemic disturbed development activities and research including AI-related research projects. AI startups, research institutions, and universities faced challenges such as delayed experiments, reduced collaborations, and limited access to resources. These aspects may have delayed the pace of innovation in the generative AI in software development market.

By Component Analysis

Based on the component the generative AI in software development market is segmented into solutions and services. The Services segment of the component holds the largest revenue share of the market.

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.

By Deployment Mode Analysis

Based on deployment mode, the generative AI in software development market is segmented into on-premise and cloud-based. The cloud deployment model segment accounted the largest share of 60% in generative AI in software development market in 2022.

The cloud-based segment is currently dominating the generative AI in software development market by deployment mode. This is owing to the rising adoption of cloud-based technologies in software development and the benefits of cloud-based deployments, such as scalability, flexibility & 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. On-premises deployment mode segment is also expected to see significant growth in recent years, as some software developers prefer to keep their AI infrastructure and data on-premises for security and regulatory reasons or to take advantage of both cloud-based and on-premises deployment modes.

By Application Analysis

Based on the application, generative AI in software development market is segmented into code generation, code optimization, bug detection, testing and quality assurance, and other applications. Generative AI is developing technology that has the potential to transform the software development process. It influences machine learning algorithms to assist different features of software development such as bug detection, code generation, documentation, testing, and others.

The code generation segment holds the largest revenue share of generative AI in 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.

Generative AI in Software Development Key Market Segments

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

Growth Opportunity

The growth opportunities for generative AI in software development are significant, and there are several areas where generative AI has the potential to transform the industry. AI can automatically generate code based on high-level descriptions or specifications, which reduces the manual effort required for coding. This can be useful for generating repetitive tasks, boilerplate code, or even entire modules or functions. It can accelerate development cycles and free up developers to focus on more composite and creative features of software development.

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.

Latest Trends

Automated Testing and Bug Detection:

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.

Bug Detection and Code Optimization:

Generative AI can help in identifying security weaknesses, bugs, 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

North America Accounted for the Largest Share of Generative AI in Software Development Market in 2022.

One of the largest markets for generative AI in software development market is in North America. North America holds the largest revenue share of 36.6%. 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 software development processes, 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.

Europe has been rapidly accepting generative AI in software development. Countries like Germany, the United Kingdom, and France have observed an increase in the adoption of generative AI tools in different industries, including software development. The European Union's focus on digital transformation and AI initiatives has further encouraged the adoption of generative AI technologies.

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

Market Share & Key Players Analysis

The generative AI in 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 finance market, with a significant share of the market.

Market Key Players

• 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

Rise of Generative AI: There's been a significant increase in the use and influence of generative AI, especially transformer-based models, in various fields including software development
Integration of AI and Automation Tools: AI and automation tools, such as machine learning-powered security checks and CI/CD pipelines, have become an integral part of the developer workflow. There's an increase in the use of tools like CodeQL on GitHub

Generative AI Coding Tools: There's been a rise in the development of generative AI coding tools that require training AI models on large amounts of code across programming languages. The use of deep learning methods allows these models to recognize patterns, make connections, and draw inferences, emulating the way humans learn patterns

Large Language Models (LLMs): LLMs like OpenAI’s GPT-3 and GPT-4, and Codex models have been trained on an enormous amount of natural language data and publicly available source code. Tools built on these models, like ChatGPT and GitHub Copilot, can produce contextually accurate outputs

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