Artificial intelligence (AI) in software development

For readers in a hurry:

• Increased efficiency through AI: automation of routine activities and code generation significantly reduce development time and increase productivity.
• Tools and support: AI tools such as GitHub Copilot, Cursor AI or Replit help developers with code creation and optimization and act like virtual assistants.
• Challenges and potential: Despite the advantages, the role of the developer remains irreplaceable, especially when it comes to the exact formulation of requirements and the system architecture.
• Future outlook: AI promises deeper integration into software development, but will still only complement human developers, not replace them.

The introduction of AI has profoundly changed the way software developers work. From automating routine tasks to generating code for the application and intelligently detecting bugs, AI will significantly increase productivity and efficiency in software development. In this article, we will explore how application developers can benefit from artificial intelligence, what tools are available to them and how the use of AI is impacting the industry.

What does software development with AI mean?

AI-supported software development refers to the use of intelligent tools and models that support programmers in the creation of applications, for example by offering generated code. The aim is to shorten development times by having AI-supported tools take over tedious routine activities. The process begins with developers defining the functional requirements of an application, similar to a requirement or functional specification. The AI can then take over some of the programming work by automatically generating boilerplate code and basic structures. However, it also generates entire application parts, thereby increasing the developer's productivity by several factors.

What is boilerplate code? This refers to standardized, repeatedly created code snippets in software development that are used in various projects or parts of an application to ensure that software can run at all. They are used to quickly initialize, configure and provide basic functions in order to increase efficiency and avoid errors. Examples include the structure of an HTML page or basic methods in programming projects such as database access.

Basics: Artificial intelligence in software development

Artificial intelligence (AI) in software development is based on algorithms and models: Algorithms are sequences of instructions for solving tasks, while models are mathematical representations that recognize patterns and enable predictions. These models are specifically trained to automate tasks such as error detection and code optimization. The models from major AI providers such as OpenAI and Anthropic offer this as standard. They have been pre-trained using the development pages of GitHub, Stack Overflow or Reddit and then perfected through feedback from countless software developers.

A comparison: classic, low-code and AI-driven development

To better understand the advantages of AI-driven development, it is worth comparing it with traditional and low-code development methods. The traditional development process is based on manual programming, which enables flexibility and customized solutions, but is time-consuming and cost-intensive, as almost every line of code has to be programmed manually by the software developer.

Low-code platforms, on the other hand, offer ready-made modules that are used by visual drag-and-drop. This speeds up development and requires less programming knowledge, but is more suitable for standardized applications.

AI-driven development uses algorithms and models to generate complete code - as in the traditional development process. However, the AI optimizes it during creation, detects any errors - even during commissioning in the data center or cloud environments - and immediately suggests suitable solutions. This allows it to significantly reduce development time (similar to low-code and even more so), while at the same time creating flexible and customizable solutions (as in traditional development).

Advantages of AI-supported software development

The use of AI in software development offers numerous advantages that optimize the entire development process:

• Lower costs and higher implementation speed: A significant proportion of programming work in modern applications consists of repetitive tasks and boilerplate code that is necessary for the application to function but has little influence on the main logic. AI tools take over this work and thus significantly reduce development time. They also create the program code for the application, including user interfaces, business logic, database access and interfaces.

• High-quality code: AI tools access huge amounts of data, for example from platforms such as GitHub or Stack Overflow. Pre-trained with these data sources, the AI can generate code that is well documented and tested. Developers can therefore access solutions that have already proven themselves in practice more quickly. Feedback loops with developers during training have also ensured the quality of the code created. 

• Automated integration of frameworks and technologies: Modern applications often use a variety of frameworks and libraries. The integration and correct use of these technologies often requires a great deal of effort and expertise. AI-supported development platforms facilitate this integration process by automatically recognizing which frameworks are suitable for certain functions and integrating them seamlessly. In this respect, they significantly broaden the developer's horizons and expertise.

• Enhanced productivity and knowledge base: AI helps developers integrate new technologies and implement best practices, which expands their knowledge and opens up new possibilities. AI acts as an assistant, making the developer more productive and versatile.

How can software developers benefit from AI?

What AI tools are available to developers?

Software developers today have access to a variety of AI tools (ChatGPT, Tabnine, Kite, etc.) that support them in their daily work. One of the best known is GitHub Copilot, which acts as a kind of virtual assistant that helps developers write code. These AI-powered tools analyze the existing code and generate suggestions that can significantly increase productivity. By using AI, developers can complete complex coding tasks faster and more efficiently, which ultimately leads to higher quality code.

How does AI improve developer productivity?

Artificial intelligence increases productivity by automating tasks that would otherwise be tedious and time-consuming. Thanks to AI, application developers can focus on creative and architectural aspects while the implementation is done by AI in record time. AI analyzes the functional requirements, creates the necessary code, recognizes patterns and suggests improvements, which increases the accuracy and efficiency of software development. By integrating AI into existing workflows, complex projects can be completed faster, making software developers more productive. This reduces project costs and pleases the customer.

How does the use of AI-supported platforms affect software development?

The use of AI-supported platforms has a significant impact on software development. These platforms offer developers the opportunity to react more quickly to market changes and develop their products more efficiently. Arrogant "top dogs" also have to fear agile competitors who simply clone their application and bring it to market with significantly more customer-oriented functionality. AI-supported systems also help to detect and rectify errors at an early stage, create developer tests and document the code comprehensibly and in full, which significantly improves the quality of the software. By automating tests and analyses, developers can receive feedback and make adjustments more quickly, which shortens development cycles. Overall, the spread of AI in software development leads to an accelerated innovation rate in the industry and lower costs.

Challenges of software development with AI

While the integration of artificial intelligence (AI) into the software development process promises significant improvements in efficiency and innovation, it also poses a number of challenges. For this reason, the specialist knowledge and expertise of an experienced developer plays a crucial role.

The challenges include: 

• Accuracy of fit and control: Although AI generates a large amount of code, this must be iteratively adapted through further specifications and additional requirements on the part of the developer. The developer "dictates" or writes, the AI improves and expands.
• Dependence on requirements quality: The effectiveness of the AI depends heavily on the quality of the requirements description that the software developer passes on to the AI. The more precisely the developer can formulate the requirements, the better the result. This process continues until the application meets the developer's requirements. 
• Complexity in demanding projects: In highly complex and innovative projects, the software developer must fully penetrate the interaction of the components in order to ensure the best possible architecture. They must also point out weaknesses to the AI that they have discovered themselves so that it can rectify them. In this respect, the developer's specialist knowledge and expertise are essential.

Practical example: Software development with AI

A logistics company needed software that could process data from different countries in real time. The platform had to bundle information from multiple sources in order to efficiently control transportation processes and avoid delays.

The role of the developer remains central

A common misconception is that AI-supported tools could make developers superfluous. As has already been made clear in the section on challenges, this is not the case: the role of the developer remains central and irreplaceable. Although AI takes over the implementation and increases efficiency enormously, the conceptual and architectural decisions are still the responsibility of humans.

AI does not simply execute blindly, but must be controlled in a targeted manner and improved iteratively until the solution meets all the desired requirements. Human intuition, in-depth specialist knowledge and the ability to think about the project from the user's perspective play a decisive role here.

Developers must therefore still have a deep understanding of the software architecture, the requirements and the interaction of the components. They are the ones who define the framework within which the AI works and ensure that the application fulfills the desired purpose. This interaction between humans and AI creates an efficient and intelligent development environment that builds on the developer's wealth of experience and the speed of the AI.

Outlook: The future of software development with AI

Development with AI tools is still in its infancy, but progress is already impressive. Future developments could enable even closer collaboration between humans and machines. The focus is increasingly being placed on greater automation of testing, debugging and deployment processes, which could speed up and simplify the development process even further.

Nevertheless, the developer's task remains to develop the vision for the application, define the architecture and find creative solutions to complex problems. The role of AI will be to take over the "manual work" and relieve the developer of the really value-adding tasks.

About Business Automatica GmbH:

Business Automatica reduces process costs by automating manual activities, increases the quality of data exchange in complex system architectures and connects on-premise systems with modern cloud and SaaS architectures. Applied artificial intelligence in the company is an integral part of this. Business Automatica also offers automation solutions from the cloud that are geared towards cyber security.