AI-powered tools and coding assistants have become popular and widely used among software developers for several reasons. They promise to deliver not only speed and comfort but also a tool for filling knowledge gaps in engineering teams that are short on time and resources. You can leverage such tools to generate code that handles complex logic and builds applications quickly.
However, while AI coding assistants might help you ship your code to production faster, they might also be the reason for inferior code quality and security loopholes in the application, since they can generate inefficient, poor-quality code and even push security vulnerabilities right into production. Hence, although it offers benefits, you should also consider the downsides of AI-generated code as well.
In this article, we’ll examine AI-generated code, what it is all about, its benefits and downsides, its challenges and the strategies you can adopt to mitigate such challenges.
What is AI Code Generation?
AI code generation, as the name implies, refers to the use of artificial intelligence to write and improve source code. AI-powered tools can help developers generate boilerplate code, fix bugs, write unit tests and even refactor source code.
Here are the key benefits of AI-generated code at a glance:
- Accelerated Development Cycles
- Faster Time to Market
- Enhanced Developer Productivity
- Enhanced Creativity and Innovation
Many developers worldwide have been leveraging AI-powered tools to expedite software development. And, there are a plethora of such tools available for you. For example, while GitHub Copilot can help you write code and fix bugs, you can use ChatGPT to generate the architecture and design, and also the source code of a complete application.
Why Can AI-Generated Code Not Often be Trusted?
The quality of AI-generated code often depends on the quality of the training data; however, the lack of real-time testing and proper model validation for real-time use are reasons you may not often trust such code. The AI models generate code based on patterns found in vast code repositories, which may often contain low-quality, non-ideal code for real-time use. The AI-generated code may contain syntactically correct chunks, but it will likely contain defects, inefficient algorithms, and even complex logical constructs, making it hard to maintain and debug. Another challenge for developers is spaghetti coding, in which code is complex and/or inconsistent with the developer’s coding standards. Without proper code review, AI-powered tools can introduce defects over time, thereby increasing technical debt.
Security in AI-generated code is a yet another issue, as AI coding assistants can introduce security vulnerabilities. While AI-generated code can help expedite the software development process, you should make a trade-off between speed and security. You should be able to maintain high standards in source code security and quality throughout your development pipeline, while harnessing the power of AI and AI-powered tools.
Why AI Fails to Deliver Secure Code?
Three key reasons why AI models struggle with security:
- Security Vulnerabilities in Training Data: AI models are basically trained on loads of publicly available code repositories that contain many security vulnerabilities and inefficient code. As a result, the generated code can often fail to produce secure, efficient code.
- Inefficiency Risk: The over-reliance of today’s software developers on AI-powered tools will make them inefficient, as they will lack security awareness and be unable to write code that addresses security vulnerabilities. Moreover, since AI-powered tools will handle implementation details, developers may lose familiarity with secure coding practices and techniques.
- Lack of knowledge on security goals and perspectives: The AI-powered tools lack a proper understanding of the business logic, design and architecture and security requirements of your application. Hence, when you generate code using these tools, they may not meet your code security goals and perspectives.
- Increased Attack Surface: The complexity and layered architecture of AI systems may increase the attack surface and potential entry points for attackers.
How can you Minimize the Risks?
To manage these risks, you should have a multi-layered approach as discussed below:
- You should conduct human-led code reviews, especially in security areas, to supplement AI-generated suggestions.
- You should use AI-powered code review tools that go beyond syntax checks to detect logic errors, performance issues and known security vulnerabilities.
- You should update security policies and threat models to account for AI-specific risks, such as hallucinated dependencies and stale libraries.
- You should educate your developers and raise awareness around AI-generated code limitations to avoid blind trust and encourage critical thinking on generated output.
- You should foster a culture of collaboration and communication between your team members so that they can share knowledge for better learning and innovation.
- You should automate security scanning and vulnerability detection tools in CI/CD pipelines to detect quality and security defects in the AI-generated code.
The Challenges
As AI and AI-powered tools galore and become increasingly prevalent in modern software development, importance of code quality and security in AI-generated code cannot be underestimated.
While these tools are helpful for quickly providing brief code snippets, they lack the human expertise and broader understanding needed to ensure the produced code is high-quality, safe and easily maintainable.
Conclusion
By using a few lines of natural language, developers can generate complete source code for methods, scripts, or configurations—much faster than if they had done it manually. However, this speed, efficiency and flexibility come with a trade-off. There are code quality and code security concerns as well – your AI-generated code often lacks quality and isn’t always secure. To mitigate these risks, you should take the necessary steps by implementing effective, practical techniques and governance.