AI Code Generation FAQ – 2024 Edition

With the rise of generative AI, it was only a matter of time before AI code generation picked pace. In fact, a GitHub survey revealed that 92% of US-based developers use AI coding tools inside and outside work.

Yet considering the mixed reviews on using generative AI tools in different fields, it’s normal to hesitate before using it. And if you’re a business owner, you may fear any consequences that may impact your apps.

So, to put your mind at ease, here are answers to common coding and artificial intelligence questions.

What is AI Code Generation?

AI code generation entails the use of artificial intelligence and machine learning to create code.

Basically, developers provide input explaining what their code should do. Based on these prompts and general best practices, an AI code generator provides code suggestions which helps developers code quicker and more efficiently.

While ChatGPT is a more renowned, 55% of developers use GitHub Copilot as it can generate code in various programming languages. However, more AI code generators are making their way to the top such as Google Gemini.

How to Use AI to Generate Code?

Most AI and ML models analyze prompts in natural language for AI code generation. If you used ChatGPT before, you’re already familiar with this process. You simply type sentences explaining the issue at hand and the model will provide suggestions.

Other tools may offer autocompletion as a method for generating code. Developers can type in code and have the model ‘think’ of additional code which they can reject or accept, and then modify if needed. Experienced programmers tend to prefer this method as it speeds up the dev process.

A third way for generating code via AI is using AI chatbots. These help developers explain code, improve syntax, and generate tests among other coding tasks. With a little back and forth where needed, developers can enhance their code to meet clients’ requirements.

Is Generative AI for Coding Only?

While generative AI is mostly used for AI code generation, there are more ways to use it –

• Streamlining Requirements – AI models can be quite helpful with requirements documentation. For instance, it can help detect incomplete or unclear requirements.
• Analyzing Code – Developers can use AI code generators to detect patterns that can lead to bugs. These tools can also suggest lines of code and functions to optimize the development process.
• Translating Code – Some tools can help developers translate code from one programing language to another. This saves time, especially in projects that will be rolled onto different devices and platforms.  
• Automating Tests – Even testers can benefit by generating tests that assess and maximize code quality. This speeds up the validation process without compromising on consistency and reliability.

How Benefits Does an AI Code Generator Offer?

The beauty of AI code generation stems from its ability to benefit developers and business people alike.

Benefits of AI Code Generator for Developers

• Accelerated Development Cycles – AI coding tools automate code generation, speeding up writing code. In fact, McKinsey revealed that developers using generative AI can code twice as fast.  
• Higher Productivity – Generative AI’s machine learning models can boost developers’ productivity by automating repetitive tasks and even generating blocks of code. This gives them more time to spend on innovation and strategic thinking.
• Code Democratization – AI tools have been very helpful in connecting technical and non-technical individuals. They also promote inclusivity as they enable novice developers to produce codes without in-depth coding expertise.

Advantages of AI Code Generation for Business

• Faster Time to Market – As it expediates the development lifecycle, AI code generation can reduce time to market. This will give your solution a competitive advantage.
• Robust IT Solutions – AI code generators can speed up code reviews as well as maintenance. Majority of these models can further analyze patterns in code to identify bugs and inconsistencies, along with suggestions to counter them.

Are There Any Risks to Using AI Code Generation Tools?

Even while using the best AI to help with coding, developers need to be involved and vigilant with the generated code.

The quality of code may vary. Especially since AI models’ understanding of business logic or certain domains may be limited. Therefore, developers will need to thoroughly check code to ensure its alignment with project standards among other factors.

Is it Easy to Tell When Code is AI Generated?

Yes, it is. Certain tools can identify coding patterns by comparing them against databases containing AI-generated code.

Moreover, software such as BlueOptima’s Code Author Detection can detect code created using AI. These solutions can even go beyond identifying code to pinpointing weaknesses in scripts.

Will AI Take Over Coding Jobs?

It’s natural for developers to worry about their jobs. There are many headlines claiming that AI code generation will make this profession obsolete. However, AI won’t be taking over coding jobs just yet if at all.

There’s more to writing code than just fluency in programming languages, which is what AI has to offer. Humans are still essential for understanding, reviewing, and maintaining code. Without them, the quality of scripts may be compromised.

Human developers are also essential to prevent spaghetti code, i.e. code that’s unstructured and difficult to read.

Can AI Generated Code Compromise Security?

Yes, if developers solely rely on AI code generators to create scripts. Without proper quality assurance and validation, your code could be at the risk of having –

• Code Vulnerabilities – Security issues may arise from the code itself. Since AI models learn from existing coding patterns, it can generate insecure code containing issues such as buffer overflows or SQL injection flaws. Moreover, AI code generation lacks contextual awareness, making it overlook critical security requirements.
• Data Privacy Issues – As AI generators need access to a project’s codebase, data privacy may be at risk. Unauthorized individuals can intercept data transmitted to tools’ remote servers, or even directly hack into servers to exploit confidential information.
• Third Party Dependencies – As models use external APIs and libraries, these come with their own set of security issues. Without vetting them thoroughly, developers can integrate unpatched vulnerabilities into the primary project.
• AI Tools’ Bias – AI code generation is susceptible to coding practices bias. Based on what the model has learned, it can make suggestions that compromise the security of software solutions.

Have More Questions Related to AI?

DPL’s artificial intelligence experts and coding professionals are here to guide you on leveraging AI for your next solutions.

Whether you wish to create an AI mobile app or a platform that taps into the power of machine learning, simply use the form below.