The history of system development has always evolved alongside technological progress. It all began in the 1970s with the Waterfall Model, a development methodology where processes such as requirements definition, design, implementation, testing, and operations are carried out in sequential stages. The next step doesn’t begin until the previous one is completed. This method worked well for large-scale enterprise or government projects where specifications were clearly defined upfront. However, its rigidity made it difficult to adapt to changes once development was underway.
In the 2000s, a more flexible approach began to gain attention: Agile development. Agile emphasizes short iterations of development and testing, allowing teams to respond quickly to changes and customer feedback. Frameworks like Scrum and Extreme Programming (XP) became widely adopted, shifting the focus toward continuous collaboration and responsiveness to real-world needs.
More recently, DevOps has emerged as a common practice. DevOps bridges the gap between development (Dev) and operations (Ops), promoting continuous integration (CI) and continuous delivery (CD). This approach enables faster and safer releases, supported by infrastructure automation and the rise of cloud computing. Infrastructure as Code (IaC) has also become a standard practice.
Today, a new wave of transformation is underway with the rise of AI in development. Tasks that once required manual labor—code generation, testing, specification reviews, even UI suggestions—are now being assisted by AI. Tools like GitHub Copilot and ChatGPT are being integrated into developers’ daily workflows, signaling a new era.
We are now standing at the threshold of a future where development is accelerated by AI, moving beyond Agile and DevOps. In this new age, the ability to continuously adapt to change is becoming the most essential skill for any developer.
