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AI / DevOps / Software Development

AI Will Create Demand and Empower Developers, Not Replace Them

Rather than fearing AI as a replacement for human developers or even traditional code, we must look at the transformative value and risks.
Dec 6th, 2023 8:54am by
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We all know AI isn’t just the future, it’s the present. In the world of software development, it’s already here.

Rather than fearing AI as a replacement for human developers or even traditional code, we have to look at the transformative value and risks of this technology when it comes to the DevOps process. Then we’ll come to understand that AI requires developers’ expertise to guide the way — thus creating more, not less demand for them. And it carries benefits that empower developers in their careers.

Generative AI is already bringing many benefits to developers, and when used with Clean Code best practices, it has tremendous potential to help create secure, maintainable and reliable software. However, there are drawbacks that must be considered with the benefits of generative AI tools.

GenAI Brings Opportunity for Software Development 

Since its inception, everyone’s been debating the benefits and drawbacks of generative AI. That’s a fair conversation. As we adapt to and adopt this technology, the future of AI includes a lot of unknowns. Here’s the thing: In the interim, we can figure out the best way to use it in the present.

There’s no question that AI can play a useful role when it comes to development. Studies like this one from Brown University report that using AI to build software shows promise. I agree based on our first-hand knowledge at Sonar, but I also approach the promise of AI with a degree of caution when it comes to the coding side of things because AI writes for speed, not for quality, which carries risk. More on that below. But, first, let’s talk about the beneficial role of AI in supporting human developers.

AI Empowers Software Developers

AI takes care of the annoying, tedious, routine tasks that may otherwise take up a significant amount of developers’ time, so they’re able to better concentrate on the real work at hand. In fact, 92% of developers are already using AI to lighten their load. From here, there are a few specific categories where AI can best aid developers:

  • Generating code snippets: AI can do this in seconds. All a developer needs to do is tell the technology what they want to accomplish and what language they want to use, and they can get some understanding of the best approach to their problem.
  • Learning: Although there’s a need to check for accuracy, AI can help developers understand code snippets and programming concepts without having to do the research themselves.
  • Documentation: Nobody likes documentation. It’s tedious and difficult. However AI documentation can help bring attention to things that didn’t work during the development process while reducing development times in the aftermath.
  • Code quick-starts: This gives a major leg up to developers who have an idea but don’t know exactly where to begin. AI can generate coding within seconds despite the language. Even if the parameters of the project need a review, it gives you a head start.
  • Algorithmic assistance: Algorithms are difficult. The great thing about AI is its ability to help developers learn and understand what they’re working with. Through pseudocode and step-by-step instructions, this tool can guide our coders through their most important projects.

AI Carries Risk that Requires Developer Expertise

While AI can free up developers’ time to work on higher-value projects and help with their productivity, it has inherent risks that require human skills to keep it in check. This is where the demand for developers increases, not decreases, in the age of AI. Here are a few of the pitfalls to watch out for:

  • What’s the source? One problem with AI is that it reduces the ability to hold people accountable for code. AI works off what it’s taught, which means it combines too many sources to locate one specifically when something goes wrong. It’s hard, if not impossible, to understand whether coding came from a person directly writing it or from someone who published their code to the internet, which makes solving problems ultimately harder.
  • Vulnerability: Again, because of its ability to crowdsource, there’s no guarantee that the coding AI generates is safe or clean. What it creates could have the kinds of bugs and vulnerabilities your team has tried hard to prevent. Security issues, particularly, could put your company in a quagmire. The need to check AI coding creates more work for software developers.
  • Quality: AI is automatic. It doesn’t gut-check or double-check for quality. Just because AI generates coding doesn’t mean that it’s as efficient or high-quality as possible. Teams have to sort through what AI creates to ensure it’s the right coding for their project or company.
  • No context: Losing the human element naturally means that you lose the context of a problem or project. AI is automatic; it doesn’t assume to know the nature of each goal you’re trying to achieve. It’s trained on generally sourced code, often obtained from unknown sources. Even if it helps reduce some of the grunt work, it still must be double-checked to ensure it’s getting the job done in full.

There is a world of possibilities to unlock with AI, but not unless we include the human element at the forefront as we harness its potential and check the drawbacks of the technology. There is transformative potential for the software industry and software development, but to let it run without any checks isn’t wise, especially when modern business success depends on the strength of the software that underpins it.

For now, our best course of action is to keep carefully monitoring and better understanding AI so that we can put it into practice responsibly. And as we do, it’s critical to keep in mind how AI works not as a replacement for traditional coding, but rather as an asset that can work alongside it.

With increased AI reliance, organizations must also remain proactive in scrutinizing generated code and averting post-production issues by following Clean Code best practices. When developers take a Clean as You Code–approach with their code — human- or AI-generated — they can ensure that it is fit for development and production and meets the required standards of their organization. With this, developers can be confident in their software, that it is consistent, intentional, adaptable and responsible — easy to understand and change, operates smoothly at runtime and contains no technical debt, therefore making it fit for purpose and making software a long-term business asset.

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