AI Code Generation: 6 FAQs for Developers
There’s no denying that AI code generation is poised to transform software development. Studies are calling attention to the incredible value this technology brings for boosting productivity in the development process. Its use is bound to grow as developers are tasked with writing more code than ever, while still ensuring they build top-quality software free of bugs, code smells and security vulnerabilities.
However, it’s still new technology, and many are unclear how to best and most responsibly use it. I’ll break down what AI code generation is, questions you should be asking before using any of these tools and how you can reap its benefits while maintaining high-quality outcomes.
What Is AI Code Generation?
The main benefit of AI code-generating tools is their ability to take tedious work off the plates of burned-out development teams. Powered by both AI and machine learning (ML), these tools use advanced large language models (LLMs) and generative AI to create or suggest code based on input from developers about their desired functionality. It is useful in streamlining repetitive tasks and reducing manual coding efforts. This code isn’t necessarily perfect — but it does provide a solid starting point that helps developers focus on more critical parts of a project. And all developers can use it, regardless of their experience level.
So, How Does It Work?
AI code generation leverages ML algorithms trained with large amounts of existing source code, often sourced from open source projects. LLMs play a crucial role in this process by comprehending the patterns and structures within the code, enabling them to generate new code segments based on developers’ specifications. This happens through three main methods:
- An autocomplete feature, where developers initiate code writing, then let AI complete the code based on patterns learned from a training data set.
- Natural language input, where developers explain their intentions, which prompts AI to generate code suggestions that help meet those goals.
- Direct interaction, in which developers use a chat interface to issue specific requests or bug-fixing commands.
How Did We Get Here?
The feasibility of AI code generation is greatly enabled by advancements in LLM, natural language processing (NLP), deep learning (DL) and neural networks technology. These systems extract insights from a variety of data sets sourced from publicly available (largely open source) code repositories.
This means that collaboration and transparency within the programming and development communities are responsible for promoting more efficient development. This leads to streamlined coding workflows, better prioritization of project components and a significant boost in overall code generation.
As these technologies advance and are refined, there’s a significant implication: the possibility of flaws still exists. Regardless of whether code is written by humans or generated by AI, achieving bug-free software remains a challenging objective. Developers must adhere to best practices by conducting thorough code reviews to help ensure the highest code quality.
Which Tools Should I Use?
Armed with these capabilities, developers have a wide array of AI coding tools at their fingertips — and the market is continually growing. GitHub Copilot, ChatGPT and GPT models, Amazon CodeWhisperer, Google AI Code Generation, CodeLlama, TabNine… Any developer who isn’t using these tools (when they make sense) is overlooking opportunities for doing better, more efficient work.
With numerous options available, developers must select the right tools to maximize the positive impact of AI. Here are a few factors to consider before adoption:
- Which programming languages does this tool support?
- How does it integrate with your IDE?
- Which specific features do you need, and which tools have them?
- How accurately does the tool suggest code completions or generate code snippets?
- How much does it cost? How quickly can you use it?
- What about security and privacy? Where do you see potential risk factors?
The cornerstone of AI implementation lies in responsibility. Companies cannot sacrifice quality for the sake of efficiency. Again, it’s crucial that reviews of AI-generated code are accompanied by thorough testing, analysis and review. The sooner developers and DevOps can conduct thorough processes, the better.
Why Is AI Code Generation Useful?
There’s a lot to like about AI code generation and multiple reasons why it’s become so common in software development. Already, 92% of developers are taking advantage of these tools. That speaks volumes, especially as businesses become more dependent upon software and demand more, faster.
According to Stack Overflow research, developers report a 33% increase in productivity when using AI-assisted tools, allowing them to focus on more priority aspects of their projects. But speed isn’t the only benefit. AI assistants are fully capable of helping developers with several parts of their work:
- AI plays a crucial role in instant coding support. In real time, developers can access suggestions and generate code snippets as they navigate the intricacies of coding.
- These tools massively boost productivity, automating repetitive and tedious tasks that may not need full human attention.
- AI drives the democratization of software development, allowing those with less experience to help create complex applications. Consequently, this broadens the talent pool at a time when developers are more valuable than ever.
What’s the Catch?
As with everything, AI has drawbacks. Developers must understand that these tools are not a magic bullet, and people will always be a valuable part of the software-building process. Studies have documented the importance of responsible use. So, as developers continue to harness this technology, what key considerations should they bear in mind?
- Code quality and reliability are critical. Don’t trust everything AI churns out. While it’s generally reliable, this tech is still capable of producing buggy, insecure code and always needs human review. A university study found that out of 164 problems tested using GitHub Copilot, nearly 30% were solved correctly, about half were partially correct and 20% were incorrect. Those numbers are significant.
- Maintainability needs attention. AI might produce code that’s more complex than necessary in the interest of fulfilling a developer’s request. Over-engineering may lead to unnecessary abstractions, excessive layers of code or overly intricate logic that breaks down when reused. This slows down future maintenance and adds a layer of difficulty to developer collaboration.
- Technical debt remains a substantial problem for companies across industries. While AI can help generate the significant amount of code businesses are asking for, that doesn’t mean it’s optimal in terms of quality, efficiency or maintainability. Correcting bad code can consume the time that AI aims to liberate developers from, potentially resulting in code that is intricate to comprehend, alter or expand.
- Loss of control is a concern for some developers who worry about overreliance on AI. This technology isn’t a replacement for human judgment or critical thinking.
How Can I Successfully Harness the Power of AI?
AI code-generating technologies aren’t going anywhere, but using them properly requires caution. Be cautiously optimistic about these tools while understanding that a strategic approach is imperative.
Companies that start small and learn through experience have the power to see where this tech works best and where the human touch remains essential. Diving in too quickly or investing too much time and resources into these tools right away may create problems that don’t yet exist.
Education is also necessary. Developers of every skill level are capable of using this tech to improve their output and performance, but use cases look different for junior-level developers versus those with decades of experience. Teach your people what looks best for you.
Finally, companies cannot deprioritize code quality. AI-generated code isn’t intrinsically perfect. Static analysis, testing and validation procedures are always necessary to ensure clean code — code that’s easily understandable, portable and capable of changing through structure and consistency but remains maintainable, reliable and secure to achieve performance demands. That’s where tools like SonarQube, SonarLint and SonarCloud can act as an AI code generation safety net. These quality analysis tools seamlessly integrate with popular coding environments and CI/CD pipelines, providing in-depth insights into the quality, security and maintainability of code, AI-generated or not.
AI is a valuable tool for developers and an unmissable opportunity for businesses looking to create top-quality software. But only by ensuring thought and care can companies make the best use of these tools and allow their developer teams to perform and shine.
