Imagine a world where AI can write and automate code. A world where developers can simplify repetitive tasks and enhance code quality - all in the quest for even greater productivity, efficiency and development. 

AI code automation presents developers with exciting possibilities for their software development capabilities to fly higher, despite still technologies still being on the runway ready for take off.

And these possibilities are big: the potential to rewrite the approach to code development, deployment and testing: the flightpath for releasing apps, products and services out in the wild, swiftly, safely and at lower cost.  

But there remain limitations: AI still has dependency issues, code quality problems and hallucinatory biases where Large Language Models (LLMs) generate information based on absent or false training data. 

In this blog, we dive deep into the fascinating world of AI code automation. We'll explore the opportunities and challenges, debunk the myths, and examine its impact on software development. Our White Paper also explores in further detail the potential of many other areas where enterprises can harness AI to automate key operations.

The journey to AI code started with a single click 

AI code assistance in software development has been present since the release of GitHub Copilot in October 2021. Copilot revolutionised the development landscape by providing developers with code suggestions tailored to their codebase.  

Since then, several AI code assistants have emerged, including:

• CodiumAI
• Tabnine
• MutableAI
• Amazon CodeWhisperer
• OpenAI Codex

These assistants have gained traction within the industry, serving as valuable tools for enhancing productivity, writing cleaner code and accelerating software development.  

AI Code Assistant Benefits: 

Time-management: Code assistants are becoming more sophisticated as their algorithms continue to improve, providing more accurate results during development and ultimately leading to quicker development deliverables.   

Productivity: Developer productivity is forecasted to increase as code assistants can automate repetitive and time-consuming tasks, allowing developers to focus on more complex and challenging issues. 

Clean code: Through the integration of code assistants, code suggestions are based on industry best practices. Even though the provided code may not be 100% accurate it might be syntactically correct, leading to cleaner written code based on industry best practices.

Testing:  Developers spend significant time creating and maintaining unit tests. AI can identify patterns and potential issues in code. It can create test cases to reduce the need for manual testing, find unknown edge cases, aiding developers to optimise their code.   

AI Code Assistant Banes: 

Overdependence: Code assistants can have a negative effect on a developer’s skills, such as degradation, reduced problem-solving abilities, lack of understanding concepts, limited adaptability, debugging challenges and loss of personal style.   

Inaccuracies: The dependency on how reliable the training data is for AI algorithms affects how accurate it is going to be.  If the training data is unreliable then the AI output is going to be inaccurate. If the training data is biased, then AI will also be biased. Currently the latest versions of ChatGPT, GitHub CoPilot and Amazon CodeWhisperer provide correct code less than 66% of the time.   

Hallucinations: This is when instances of LLMs generate information or data that was not explicitly present in its training data. Where AI creates content that holds no factual basis or grounding in the learning it received, hallucinations present many security concerns. 

Prompt Engineering: giving AI a code roadmap 

Imagine you are a brilliant developer, brimming with creative potential, but easily sidetracked by daydreams. This is what LLMs are also susceptible to. Prompt Engineering can be used to guide their boundless energy and harness their talents.

Prompt Engineering can further automate repetitive tasks: instead of manually writing boilerplate code or simple functions, developers can use prompts to generate them instantly, freeing up time for more complex tasks. 

It enables faster prototyping and experimentation. This means developers can quickly generate different code variations based on prompts, so they can rapidly explore ideas and test different approaches. 

Conclusion - coding on another level?  

While AI code automation is yet to find its wings, there is no denying it presents a bright horizon for developers to benefit from game-changing tech in their toolkit. 

With GitHub Copilot X expecting a future vision, AI with chat and terminal interfaces could become fully integrated into every part of the development workflow.  Currently, adopting AI could help developers improve their coding standards.

It could also provide better understanding and education on complex topics and help them build better software. On the topic of education, our White Paper takes the wraps off the many benefits and challenges facing AI adoption.    

At the same time, it's worth noting that AI is not a silver bullet, but a means to aid developers in their everyday roles to help automate repetitive tasks. This is so they can focus and hone their craft into making creative and innovative software.

AI code automation might still be at ground level, but its scope to take off could be stratospheric..

Our white paper provides guidance on how to successfully implement AI programmes of work in established, governance-led enterprise IT environments.