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How generative AI will impact the software development process

Read Time 6 mins | Written by: Cole


AI-generated code is here and it’s likely to both hurt and help software development in a number of ways. On the positive side, AI can help automate many tedious and repetitive tasks that are currently performed by humans, freeing up developers to focus on more challenging and creative work. This can lead to a faster and more efficient development process. It can also help reduce the potential for human error in code.

On the other hand, AI could also potentially replace some jobs currently performed by software developers, leading to job losses in the industry. Additionally, the use of AI in software development raises ethical concerns, such as bias in algorithms and the potential for AI to be used maliciously. 

And one of the biggest unsolved issues is the large companies profiting from the intellectual property on Github, Stack Overflow, and other places where software developers have shared their work and resources for years. 

The main limitations of AI coding tools like OpenAI Codex, Github Copilot, and ChatGPT are that they are not yet able to replace the intuition and creativity of a human coder. And that they produce errors often and confidently. 

Here’s one of the main things they’re good at, a quick look at the benefits of AI-assisted coding, and the risks to navigate. 

AI can automate repetitive developer tasks

AI coding tools could revolutionize the way software development teams operate – providing unprecedented levels of efficiency and speed. As AI technologies advance, they are already being used to automate mundane coding tasks, providing software developers with more time to focus on more complex tasks.

AI coding tools can automate certain repetitive tasks, but they are not yet capable of understanding the nuances of the code or the thought process behind the development of a project. 

However, the benefits of using AI for coding are vast. AI tools can quickly and accurately complete tasks such as code refactoring, bug-fixing, debugging, and code generation. By automating these tasks, AI coding tools can significantly reduce the time it takes to develop a project, allowing software development teams to focus on more complex tasks and to complete projects much faster.  

How software dev teams can use AI productively

In order to use AI tools successfully, it is important for software development teams to ensure that humans check the outputs generated by generative AI. AI coding tools are only capable of following instructions, and cannot replace the intuition, creativity, and problem-solving capabilities of a human coder. Therefore, it is important for software development teams to review and test the outputs generated by AI coding tools in order to ensure accuracy and quality.  

Overall, AI coding tools provide software development teams with unprecedented levels of efficiency and speed. While AI coding tools cannot yet replace the intuition and creativity of a human coder, they can still provide a huge boost to the productivity of software development teams. 

By ensuring that humans check the outputs generated by AI coding tools, software development teams can successfully use AI tools to automate mundane coding tasks and complete projects much faster.

Main benefits of AI coding tools for software dev teams

  • Automated code generation: Generative AI can analyze requirements and generate code based on templates, reducing the time and effort required for manual coding.

  • Improved efficiency: Generative AI can automate repetitive tasks, freeing up developers to focus on more complex and creative tasks, and improving the overall efficiency of the development process.

  • Enhanced collaboration: Generative AI can help teams collaborate more effectively by automating the generation of documentation and other resources, reducing the time spent on administrative tasks.

  • Better quality code: Generative AI can produce code that is more consistent, reliable, and secure than code written manually by developers.

  • Increased innovation: Generative AI can provide new and innovative solutions to software development problems, leading to faster time-to-market and increased competitiveness for businesses.

Risks & Drawbacks of Generative AI

While generative AI has the potential to greatly enhance the software development process, it also has the potential to negatively impact it in some big ways. 

  1. Job loss: Automated code generation can reduce the demand for human coders, leading to job loss and potential workforce displacement.

  2. Dependence on technology: Teams may become too reliant on generative AI and may lose their manual coding skills, potentially limiting their ability to troubleshoot or modify generated code.

  3. Quality concerns: Automated code generation may produce code that is less optimized, less efficient, or less secure than code written manually by developers.

  4. Lack of customization: Generated code may not meet specific project requirements, leading to the need for manual modifications, which can be time-consuming and add complexity to the development process.

  5. Legal and ethical issues: Automated code generation can raise questions about intellectual property, liability, and the ethical implications of machine-generated code.

While generative AI has the potential to greatly enhance the software development process, it is important for teams to approach it with caution, carefully considering the potential risks and limitations. If teams decide to use these tools, they should continuously monitor and evaluate the impact of generative AI on their processes and make necessary adjustments to ensure that it is being used in a responsible and effective manner.

AI isn’t going anywhere

Overall, the impact of AI on software development is going to be complex and multifaceted. It is important for the industry to carefully consider the potential benefits and drawbacks of AI, and to develop responsible and ethical approaches to using it in software development.

For example, GPT-3 wrote most of this article (across ChatGPT, Jasper, and Anyword) and we edited it in about 10 minutes. It covers some important areas but misses big ones. Like, how AI-generated text and code are already contaminating the source material (the internet) used to train future models.

We’ll write a more in-depth examination of generative AI for coding and software development in the future. It’s going to continue to impact software engineers and Agile development processes across all industries. Feel free to share your experience with AI coding tools in the comments. We’ve seen plenty of serious errors and some convenient time savers. We’d love to hear what you’ve been finding so far. 

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Cole is Codingscape's Content Marketing Strategist & Copywriter.