AI-assisted software development refers to the use of artificial intelligence tools — including generative AI — to support a diverse range of tasks that comprise the software development process.
The best-known way AI is used in software development is to help developers write code. Coding assistants like GitHub Copilot, for example, can make detailed code suggestions. However, the use of AI in software development and delivery isn’t limited to writing code; it can be used for a range of other purposes, including documentation and designing a software architecture.
The use of AI tools to support various facets of the software development process, from writing code to testing to documentation.
It has the potential to increase developer productivity and increase the quality of the software that is being shipped.
If used inappropriately it can reduce productivity and even introduce errors and bugs to the software. It could also disengage software developers and remove opportunities for learning.
It’s being used for a diverse range of software development tasks, helping to speed up prototyping, improve testing and debugging and to document decisions.
What is it?
AI-assisted software delivery is the use of artificial intelligence tools across the software delivery process. One of the most visible examples of AI-assisted delivery is the use of GitHub Copilot, which uses AI to suggest possible code solutions as developers are writing code. However, it can be used for many other tasks, such as prototyping, automated testing and even code refactoring — it can help identify areas of your codebase that can be improved and suggest possible solutions.
What’s in for you?
The use of AI in software delivery has the potential to increase productivity and improve the overall developer experience. Used effectively, it can reduce some of the more tedious tasks software developers need to do and allow them to focus on work where they can really add value. This should mean productive software developers delivering better, high-quality software faster for your customers and users.
An organization that uses AI effectively could also be an attractive proposition for talented practitioners, helping you to stand out in a competitive market for skills.
What are the trade offs?
AI is never a silver bullet solution — that’s certainly true in the context of software delivery. If used ineffectively or inappropriately, it could make software developers less productive as they spend time checking AI-generated code and making changes to it to avoid errors. If implemented in a top-down manner it’s also possible that it could lead to disengagement — AI needs to be viewed as a complement to the way people work rather than a complete disruption of it. In other words, AI is not a tool that can replace developers; although it can act as a helpful partner in certain contexts, it would be wrong to think of it as akin to a pairing partner that can critically assess and offer feedback on your code and its design in depth.
Adopting such a perspective could have particularly negative implications for skill development and learning. If AI becomes embedded in software development tasks and processes, this may mean software developers never get to tackle certain challenges or problems head on. Not only might this hamper their development, it could even encourage bad habits.
How is it being used?
The use of AI in software development remains in its early stages. Although tools like GitHub Copilot are starting to see increased adoption, the use of generative AI for writing code remains in its infancy — practices are still fairly immature with individual developers often using it in ways they feel comfortable with.
The use of AI in other parts of the process is arguably even more nascent — however as more teams seek to leverage AI technologies, we are likely to see new products emerging on the market. We can already see this happening in areas like documentation, testing, team collaboration and knowledge management.
