If you’re in the predictive software development field, you’ve probably heard of the terms “predictive” and “adaptive” SDLC. But what do they really mean? And what’s the difference between them?
Predictive SDLC
Predictive SDLC is a traditional approach to software development that relies on upfront planning and stringent controls. In this model, the requirements are gathered at the beginning of the project, and then a plan is created accordingly. The project is then executed according to this plan. Predictive SDLC is best suited for projects with well-defined requirements where there is no scope for changes during the course of the project.
Adaptive SDLC
Adaptive SDLC, on the other hand, is a more flexible approach that allows for changes during the course of a project. This approach is better suited for projects that are more complex or uncertain, where there is more need for flexibility and adaptation. This makes it more suitable for projects with changing or undefined requirements.
Predictive vs. Adaptive: Which SDLC Model is Right for Your Project?
There are two types of software development life cycles (SDLC): predictive and adaptive. Both have their own advantages and disadvantages, so which one should you use for your next project?
That depends on your specific project requirements. If you have a well-defined project with little room for change, Intetics predictive SDLC may be the way to go. But if you’re working on a complex or uncertain project, adaptive SDLC may be a better fit.
So, which one is right for your next project? Here’s a look at the key differences between these two popular SDLC models:
Predictive vs. Adaptive: Key Differences
Planning Phase: In the predictive model, the planning phase is much more extensive and detailed than in the adaptive model. This upfront planning helps to establish clear objectives and milestones for the project team to follow throughout execution. In contrast, the adaptive model focuses on high-level goals during this initial phase, allowing for more flexibility later on.
Execution Phase: The predictive SDLC model follows a very structured approach during execution, with well-defined roles & responsibilities for each team member. Tasks are completed in linear order and progress is monitored closely to ensure adherence to the plan. The adaptive model takes a more iterative approach, allowing teams to work on tasks concurrently and make changes along the way as needed.
Monitoring & Control Phase: As you might expect given its name, monitoring & control are critical aspects of the predictive SDLC model. Project managers use tools like earned value analysis (EVA) to track progress against milestones and identify any areas where corrective action may be necessary. Meanwhile, in an adaptive environment, regular check-ins with stakeholders help keep everyone aligned on objectives and allow course corrections to be made if necessary.
Here are some specific things to consider when deciding which type of SDLC to use:
1. Requirements stability: If the requirements for your project are subject to change (e.g., due to market conditions or customer feedback), then an adaptive approach may be more appropriate. On the other hand, if the requirements are relatively stable, then a predictive approach will likely be more successful.
3. Schedule constraints: If time is of the essence and you need to complete your project as quickly as possible, then an adaptive approach may be more appropriate since it can allow for incremental delivery of functionality. On the other hand, if schedule constraints are not as critical, then a predictive approach will likely be more successful since it can provide better long-term planning and coordination among team members.
4. Team capability: If your team has experience working in an agile environment and is comfortable with responding to change quickly, then an adaptive approach may be more appropriate. On the other hand, if your team does not have experience with agile methods and prefers a more predictable workflow, then a predictive approach will likely be more successful.
