There’s no mistaking the reality of AI’s influence. Across industries, businesses are calling on tech workers to make the most of this transformative technology as demand skyrockets and companies race to overcome the competition. A flagging economy only pushes expectations for AI use—in software especially. To contend with arduous workloads, 92% of developers are already using AI code generators, according to GitHub. That’s an overwhelming majority, not to mention that this usage is poised to continue generating steam.
In light of this, we must ask ourselves a critical question: How can businesses ensure developers retain the quality of their AI-generated code? AI is a useful complement to human developers to maximize their time and allow them to focus on projects best suited to their skill sets. That said, companies have to put safeguards in place to harness the technology, not just for boosting developer productivity but also for quality outputs.
AI Can’t Replace Human Developers
AI code generators are a useful tool for a developer’s toolbox. Efficient, easy to use, and helpful (in a fairly rudimentary way in these early AI days), they give developers the ability to outsource some code-writing, which helps alleviate mounting demands as companies expect them to produce massive amounts of code — and fast. As most developers continue to buy into AI for productivity benefits, though, comes the responsibility to gut-check what these AI tools produce.
There are multiple limitations and risk considerations when it comes to AI-generating code. Lack of source knowledge and context is one of them. AI pulls from what it’s taught (i.e., past data, natural language processing, machine learning), meaning it combines too many sources to trace specific pieces of coding back to one place; it’s impossible to trace the code back to a single developer or project. Security, too, becomes an issue; you can’t guarantee that what is produced is safe or clean as is. And, finally, AI doesn’t check for quality, which increases the likelihood that the code will be difficult and costly to maintain. All this ladders up to the mounting technical debt companies are already contending with.
Developers can’t assume that any AI-generated code is of quality to be put directly into production. Human workers must review code thoroughly to ensure it meets clean code attributes–secure, reliable and maintainable. Ignoring these tenets puts businesses at risk of falling victim to the trillion-dollar cost of bad software. That said, there are still undeniable benefits to using AI in a coding environment that make it an invaluable tool for any company that wants to stay at the top of their software game.
Working Responsibly With AI in Software Development
Despite its potential shortcomings, when used safely and effectively alongside a clean code solution, AI brings a tremendous opportunity to the software space that has already made it a key tool for developers as their workloads and demands mount. For one, AI has the incredible ability to churn out code at a speed that far outpaces its human counterparts. This tech can complete tedious work that may take valuable time out of a developer’s day in a matter of seconds. AI can not only move a project forward at a faster rate, but its quick coding capabilities can also give developers a jump start on their projects and help them figure out where to begin.
While the source of AI’s knowledge base can present a challenge, the vastness of that knowledge is a benefit. As long as a developer can input their preferred language and their specific needs, AI can share the best approach to solving a problem. Its algorithmic assistance can help lead a developer through parts of their projects step by step. This also alleviates the burden of research from developers, who can spend less time checking for accuracy than they would trying to understand different programming concepts or code snippets outside their area of expertise. AI can also handle the slog of documentation, which can not only bring clarity to what didn’t work during the development process but reduce time to delivery overall.
Developers who don’t harness AI do so at the risk of falling behind the competition as this tech becomes such a vital piece of everyday work in software. But there’s no mistaking the fact that to best incorporate AI, developers still need to ensure its accuracy before any code generated by AI makes it to production.
GenAI Requires Automated Tools as Safeguards
As I previously said, developers can’t assume that any AI-generated code is clean and fit for production. That means human skills are still required to check the output. However, this kind of work can often fall to developers or DevOps, and having human workers check code on their own creates an incredible workload that can even negate the use of AI in the first place.
The best way for companies to mitigate this and mitigate technical debt is to implement automation in the part of the process that scans and monitors AI-generated code. But that can’t be done without developer buy-in. Those creating the software are the people who will best understand the development process as they work through it day in and day out, so their input is required to make the most out of automation.
There are developer tools that can handle this kind of work while still including developers in the process. For example, introducing code-scanning into the CI/CD process can not only help continuously monitor AI-generated code for bugs and issues, allowing developers to focus on other more challenging aspects of their projects, but it also gives them insight into errors and allows for speedier resolutions. These tools aren’t necessarily new to development, but their ability to identify bugs can help developers prioritize other aspects of the process, particularly as the amount of code reaches exponential levels compared to decades ago with the introduction of GenAI.
Static application security testing (SAST) also allows for the identification of security vulnerabilities that can potentially arise from AI-generated code. This is critical in the software development lifecycle (SDLC) so developers can spot vulnerabilities before the code goes into production. AI-generated code is only as strong or secure as that source, and since it’s not entirely possible to verify, developers need to take every precaution available to avoid security issues that rack up costs in the long term.
The vital part of using these tools is ensuring they’re integrated into the process in a way that makes sense for developers rather than creating additional work and headaches with a cumbersome process that doesn’t fit the way teams think and work. But, the need to check AI-generated code shouldn’t be underestimated. Developers can’t simply trust what they’re given. By including a level of automation within the development process—and doing so early and often—developers are in a position to produce better-quality, more secure software to push a business forward.
Effective Adoption of AI Will Encourage Business Growth
While it’s difficult to predict exactly where AI will take us in the years to come, it’s equally undeniable that it will play a critical part in the future of software development. Yes, AI is already helping to streamline processes and letting developers prioritize the tasks that best take advantage of their skills. But, as noted above, that doesn’t mean the code output is entirely safe, secure or accurate. Developers still need a level of automation to deliver code that best takes advantage of AI while keeping their workload manageable and their focus on other parts of the development process.
With the right guardrails in place, developers can guide AI to create the kind of clean, secure, and reliable code that aids businesses rather than create costly problems for them, both now and down the line. Human skills, with a boost from AI, plus code scanning safeguards, give organizations the winning combination to put themselves in the best possible place to succeed in a rapid, fierce competitive landscape of digital business.