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As companies look for methods to accelerate software development and empower their development teams, generative AI and low-code tools have gained traction as a means to more efficiently churn out code and deploy applications fast, freeing developers to focus on more intensive work as well as projects they’re excited about.
Amid increased adoption of these solutions, though, where does traditional coding fit in? How should developer teams and businesses discern where to focus on traditional coding methods over new and efficient tools for code creation? What does the convergence of these coding methods mean for the future of software?
No matter what the answers are, it’s undeniable that speed can never come at the cost of quality. Especially in this new era of AI, organizations cannot overlook the importance of Clean Code — code that is consistent, intentional, adaptable and responsible. Only when code is clean does it create the backbone of top-quality software that businesses can feel confident about.
No matter what industry a company operates in, its size, stage, geographic footprint or other defining characteristics, chances are that it suffers from code-induced tech debt. In 2022 alone, poor-quality software cost the United States $2.4 trillion. It’s a far-reaching issue that plagues both technical teams and business leaders alike because ultimately, profit margins are at stake. As companies increasingly rely on software, those who don’t invest in Clean Code only stand to lose.
A major problem with software that’s riddled with bad code is the price of fixing it, both financially and in terms of time and resources. After-the-fact code remediation tends to be an overwhelming, not to mention unfair, process for developers, leading to it typically going unaddressed. Taking a “Clean as You Code” approach ensures that new code added to a codebase continuously meets certain quality standards so bad code doesn’t accrue interest.
While traditional coding can be a time-consuming and lengthy process, one of its greatest strengths is the control over customization it gives to developers. Full control of a codebase gives you the power to code anything you want with no limitations, no WYSIWYG restrictions and no boundaries to functionality.
When a developer creates the source code, they have complete oversight and can ensure security and efficiency. Low code doesn’t allow access to source code for security and optimization, and with AI-generated code it is impossible to know if it’s of quality unless it’s given a close eye. This is why when it comes down to it, no matter how code is being created, it must be ensured that it meets Clean Code standards.
By focusing on the quality of new code as it’s added to a codebase, businesses can see a reduction in issues that can cause larger challenges in the later stages of development when it’s much more challenging to correct. This approach ensures software is issue-free by production, creating a much smoother application development and deployment process and a more robust, efficient and maintainable software development life cycle.
This enables developers to spend more time on the important tasks at hand. Developers see an increase in code velocity, no matter if they’re following traditional coding practices or using low code or GenAI. And as new code is added, the codebase as a whole improves over time, supporting faster time to market with new features and enhancements. The power of Clean Code is that it allows developers to do what they love: create and innovate.
Knowing how to code will always be important. Similar to medicine, while there are advances in how to treat patients and perform surgeries, doctors still must go through the required education to learn the fundamentals that makeup health care. Low code and GenAI are certainly great at lowering the barrier to entry when it comes to coding, but developers still have to know how to traditionally code.
While not everyone has access to and the money for college to learn traditional coding, there are organizations that provide developer education outside of the typical college route. But not all companies partner with these different paths to entering the job market, which only adds to an already concerning skills shortage. Potentially, low code and GenAI tools will bring more people into the field, but these newer methods won’t erase the need for developers to be educated on traditional coding.
There is clearly appeal to low code, and more and more developers are adopting GenAI tools, but traditional coding remains a must and is a secure and cost-effective method of developing enterprise applications. No matter which approach is taken to develop code, though, it should be done following Clean Code best practices.
When code is developed with a Clean-as-You-Code approach, not only can developers feel confident that they’re newly added code is issue-free, but in turn, old code is corrected.
In the very near future, traditional coding using Clean Code and generative AI will have the output velocity of low code while retaining the security and customization that make traditional coding so powerful.
