On this episode, we have Alex, Maki Villano, Edd Alc, and Charles Mejica Madronero joining us to discuss if the Software Development Life Cycle is still relevant in the age of Agile, DevOps, and AI.

The Software Development Life Cycle (SDLC) has long been a foundation of software engineering—but is it still relevant in today’s fast-moving tech world? This episode revisits the classic SDLC model in light of Agile, DevOps, and AI-assisted coding. We’ll examine how modern practices have evolved or replaced traditional phases, and whether the core principles of SDLC still hold value in current development workflows.

Do you think the traditional SDLC is outdated—or just misunderstood? (Generalization)

The traditional SDLC is often considered misunderstood rather than completely outdated. While the rigid, sequential "waterfall" model is no longer practical for most modern projects, the underlying core principles of the SDLC—like planning, design, implementation, and testing—are still fundamental. Modern methodologies like Agile and DevOps haven't replaced the SDLC; instead, they represent a more iterative, continuous, and collaborative way of executing these same essential phases. The concept is still valid, but its application has evolved significantly.

Which part of the SDLC do you think developers ignore the most today? (Generalization)

Developers today often tend to ignore the documentation and maintenance phases of the SDLC the most. In the fast-paced world of Agile and continuous delivery, the focus is heavily on rapid development and new feature releases. Comprehensive documentation is sometimes seen as a secondary task and can be neglected, leading to knowledge gaps. Similarly, proactive maintenance and long-term planning for system health can be overlooked in favor of building new features, which can create significant technical debt down the line.

How do Agile and DevOps integrate or conflict with classic SDLC stages? (Generalization)

Agile and DevOps don't necessarily conflict with classic SDLC stages; rather, they integrate them into a continuous, cyclical process. Instead of a single, long-form SDLC, they break the cycle into smaller, iterative loops. Agile focuses on delivering working software frequently and getting continuous feedback, so all SDLC stages are repeated for each sprint. DevOps emphasizes automating and integrating the development and operations stages, particularly testing and deployment, to ensure a smooth flow throughout the entire lifecycle.

Has AI development changed how we plan or test software? (Generalization)

Yes, AI development has fundamentally changed how we plan and test software. In the planning phase, we now have to consider data collection, quality, and ethical implications in a way that traditional software didn't require. Testing has also been transformed. We're moving beyond simple unit tests to focus on more complex, data-driven challenges like evaluating model accuracy, detecting bias, and ensuring the reliability of predictive outcomes. AI-assisted coding tools are also starting to change the implementation phase, helping developers write code more efficiently and with fewer errors.