Shahrzad Baraeinezhad, Service Management

Artificial intelligence in software development

In recent years, AI has had a significant impact on many industries, including software development. In this blog post, we explain the impact of AI on software development and how it is already changing the way software is created.

1.1 What is artificial intelligence?

Artificial intelligence (AI) is an area of computer science that focuses on the development of machines that can perform tasks that normally require human intelligence. Examples include understanding natural language, recognizing images and making decisions based on specific circumstances. John McCarthy, the father of AI, defines artificial intelligence as the science and technology of creating intelligent machines.

Narrow AI and General AI

1.2 Types of AI

AI can be roughly divided into two categories – narrow AI and general AI:

  • Narrow AI: also known as weak AI, refers to systems that are trained to perform a specific task or problem. An example of narrow AI would be a speech recognition system that has been developed to convert human speech into text, as is the case with voice assistants such as Siri, Alexa or Google Assistant.
  • General AI: or strong AI – is intended to achieve human-like intelligence and consciousness. The system is able to learn and act in a way that is intuitive for humans. An example of general AI is an AI that is able to perform a wide range of tasks such as image recognition, language processing, decision making and problem solving without being limited to a specific field of expertise.
In the past vs. today

1.3 Tools for software development

A software development tool is a computer program that software programmers use to create, modify, manage and debug various types of software such as web applications, web services, mobile applications and for all levels of programming and each phase of the software programming cycle (design to maintenance). The main purpose of any software development tool is to support programmers in creating software by helping to write error-free code, reduce workload and increase productivity.

In the past, programmers didn’t have many options to solve their challenges and speed up development. The only sources were books & technical literature, the use of websites and forums like Stackoverflow, or the help of team leaders or seniors in a programming team. Today, with the help of AI assistance tools, programmers can benefit from a variety of tool options that support all programming languages and are compatible with all types of teams, levels and systems. Therefore, with the help of AI, they can develop software faster, smarter & more efficiently and achieve this with higher quality assurance.

When selecting software development tools, there are a few factors to consider in advance. This will ultimately achieve a better result and increase the productivity of this tool.

What to look out for when selecting software development tools:

  • Flexibility
  • Fast deployment
  • Ease of use & compatibility
  • Functionalities Options
  • Suitable target group

2. The role of AI tools in software development

AI has dramatically changed the way programming is done. The main advantages of AI tools for software development are:

  • Accurate estimates
  • Efficient prototypes
  • Support with coding
  • Fast & error-free software delivery
  • Automated business analysis & better decision-making
  • Better quality & productivity and therefore more customer satisfaction

There are different types of AI assistance tools for programming with different objectives:

  • Code completion and prediction tools: e.g. Tabnine
  • Documentation/automation tools: e.g. Mintlify
  • Test automation tools: e.g. Selenium
  • Code review/assistant tools: e.g. GitHub
  • Bug detectors & code quality analysis tools: e.g. SonarQube

3. Examples of AI tools for software development

Here are three examples of popular AI assistants tools for software development that are used by many developers today for optimized coding:

  • GitHub Copilot: GitHub Copilot is an AI-based tool from Open AI. Copilot can make coding suggestions and identify errors in almost any programming language or framework. GitHub Copilot helps developers code faster with less work, focus on solving bigger problems, and feel more satisfied with their work.
  • SonarQube: SonarQube is an open-source code quality assurance tool that helps developers and organizations systematically deliver bug-free and clean code. SonarQube checks code quality by analyzing the source code, defining the number of errors and checking for security vulnerabilities at every step of the workflow. This tool can support up to 30 programming languages and different platforms. If the code is always clean, you can focus on innovation and improve the entire codebase with minimal cost and effort.
  • Amazon CodeWhisper: Amazon CodeWhisper ist ein weiteres intelligentes KI-Tool, das Programmierern während des Programmierens Codevorschläge liefert. CodeWhisper erkennt, wie Sie eine Codezeile oder eine Kommentarzeile beenden werden. Es kann eine vollständige Funktion generieren oder einen Codeblock vervollständigen. Es unterstützt gängige Programmiersprachen. Darüber hinaus verfügt dieses Tool über Funktionen zur Dokumentationserstellung und Sicherheitsfreigabe.

4. Problems & challenges of AI tools

AI tools for software programming have brought many benefits such as rapid development and deployment, increased productivity and efficient working. On the other hand, there can also be concerns and problems:

  • Data privacy and security concerns
  • Poor integration into the existing infrastructure
  • False expectations
  • Lack of control and incorrect results
  • Employee dependency
  • Costs

5. Future & outlook

The future of software development will be closely intertwined with the world of AI. There are also some questions that have yet to be answered, such as whether AI will completely replace software developers or whether software engineers will still be needed in the future!

Companies need to be clear about the future plan for AI tools. This revolution requires adaptability, continuous learning and responsible action to reach its full potential. Therefore, companies and developers need to pay attention to some key points to be ready to adapt to the future of AI technologies. By doing so, better productivity can be achieved, such as constant training and learning of AI and machine learning techniques, data science and updated tools and programming languages.

Furthermore, ethical considerations in AI, such as privacy, transparency, fairness and accountability, are essential for responsible AI development. Effective teamwork and the ability to communicate technical concepts to non-technical stakeholders are also important skills in the collaborative world of AI projects.