Predictive vs. Adaptive SDLC: What is the Difference?

Organizations are different from one another. Projects and business strategies differ from one another. Make sure your development approach matches your organization and project. Many project managers are moving away from conventional predictive Software Development Life Cycle (SDLC) methodologies toward adaptive SDLC methodologies.

Should you?

To determine this, you should have a clear understanding of predictive vs. adaptive SDLC approaches and identify the best methodology for your organization and your project.

Predictive Software Development Life Cycle: An Overview

As the name suggests, predictive SDLC assumes you can predict the complete workflow. It involves fully understanding the final product and determining the process for delivering it. In this form of project life cycle, you determine the cost, scope, and timeline in the early phases of the project.

One of the most common predictive models is the waterfall model. It assumes various phases in the SDLC that can occur sequentially, which implies that one phase leads into the next phase. In simple words, in waterfall model, all the phases take place one at a time and do not overlap one another.

While the waterfall model is quite simple and easy to use and understand, it also entails a few drawbacks that could drastically impact your project.

Since the waterfall model follows a sequential approach, once an application is in the testing phase, it becomes difficult to go back and debug it in the development stage.

Pros of Predictive SDLC

  • It is easy to understand and follow as each phase is initiated after another phase is completed.
  • The laid down instructions and concise workflow makes it easier for the developers to work within a specified budget and timeframe.
  • It enables organizations to assume the expected project budget and timelines (IF all goes as planned).
  • Each stage in the predictive SDLC has specific timelines and deliverables, which makes it easier for teams to operate and monitor the entire project.

Cons of Predictive SDLC

  • Working software is produced at a later stage in predictive SDLC, which leads to delayed identification of bugs and vulnerabilities in the application.
  • Organizations often have to bear additional costs of delayed applications if bugs are discovered in the testing phase of the project.
  • It is not the ideal SDLC model for complex projects.
  • Predictive SDLC is not suitable for dynamic projects that entail flexible requirements or uncertainty in the end product.

The main concern of a predictive SDLC approach is to develop and maintain the specifications of the final product. This makes it ideal for projects where all the requirements are defined and well understood with a clear vision of the final product.

In predictive SDLC, there are minimal expected changes as the work is already predictive and well-known. The team has a clear idea of exactly where the project is heading and how to follow the sequence.

On the other hand, a predictive approach can be extremely rigid, requiring developers to maintain strict and rigorous standards throughout the life cycle. Since the sequence of the work is already predetermined, any subsequent changes can be very costly and time-consuming.

Adaptive Software Development Life Cycle: An Overview

Adaptive SDLC approaches have a mix of incremental and iterative development. It involves adding features incrementally and making changes and refinements according to feedback. In other words, the work can easily adapt to the changing requirements based on new feedback received from the client.

Agile and other iterative methodologies fall under the umbrella of adaptive SDLC. A key element of adaptive SDLC methodologies is that while it defines certain milestones throughout the SDLC, it also allows flexibility to achieve them.

Adaptive SDLC, such as Agile, focuses on achieving the desired end goal by quickly adapting the dynamic business requirements. It puts more focus on the present requirement and leaves room for future scope of the project.

Pros of Adaptive SDLC

  • Adaptive SDLC entails iterative, evolutionary and incremental methodologies which offer flexible guidelines and easy flow of work.
  • Methodologies such as Agile are efficient in nature and enhance team collaboration.
  • Short feedback loops lead to quick adaptation to changing requirements.
  • Reduces potential vulnerabilities and bugs at the deployment stage as the application is frequently tested while in the development phase.
  • It focuses on delivering high quality applications while maintaining technical excellence.
  • Encourages different teams to work together on a project, increasing face-to-face interactions and building better work environments.

Cons of Adaptive SDLC

  • It demands for extensive client/user involvement throughout the SDLC.
  • Various teams have to work together continuously while working with adaptive SDLCs, and this involves numerous interactions. Continuous communication between teams can be time consuming and require more commitment.
  • Since adaptive SDLC requires close collaboration between organizations and their clients, lack of commitment from either of the sides could impact software quality.
  • Frequent changes are adopted just in time for development which might result in less detailed documentation.

Adaptive SDLC approaches are best for projects that have the potential for significant changes in scope or that there is uncertainty in what is desired. You may need to adapt to the changing demands of the client for these projects.

The adaptive SDLC methodology is typically faster than predictive SDLC approaches. This is primarily due to the fact that few projects are sufficiently understood to really use a predictive SDLC methodology.  When requirements are not sufficiently understood, issues are identified late in the lifecycle and this leads to expensive re-work.

Which is Better?

Since each approach has its uses for specific types of projects, there is no clear-cut decision as to which of them is better. The choice depends largely on the project type, your strategies, and organizational needs.

It’s best if you carefully analyze predictive SDLC and adaptive SDLC and weigh the pros and cons for each project rather than relying on a uniform approach for all projects.

Predictive SDLC approach may be a better choice if:

  1. You’re working on a project that the team is already familiar with. The team will be more productive since they already know exactly what is expected out of the project and what they are supposed to do.
  2. There is little chance of changes in the project parameters. This is crucial because any subsequent changes toward the end of the project will be very complicated and expensive to implement in a predictive approach.
  3. There are very well defined and understood requirements of what the final deliverable product should be.
  4. You have a thoroughly documented project development process to work with.
  5. You prefer predictability and like to have a clear idea of possible/expected changes beforehand.
  6. The project manager isn’t too experienced with other methodologies. In this case, things would go more smoothly if they worked within a familiar methodology that they have tons of experience with.

Adaptive SDLC approach may be a better choice if:

  1. You’re working on a project with evolving or undetermined parameters. An adaptive approach gives you room to make adjustments based on new or updated parameters.
  2. There is no rigid expectation as to how the final product might turn out. In other words, you should use the adaptive approach only if the project is innovative and/or exploratory in nature.
  3. You’re working with a flexible timeline.
  4. You work in a rapidly evolving industry.
  5. The project manager is experienced with adaptive SDLC methodologies.

Bottom Line

As you can see, both predictive and adaptive SDLC approaches have unique benefits so it would be a huge mistake to use only one of them for all your projects. Carefully assess each project demands and specifications to see which approach you should take.

Got any questions about predictive vs. adaptive SDLC? Let us know in the comments.


Cypress Data Defense was founded in 2013 and is headquartered in Denver, Colorado with offices across the United States. Our goal is to help organizations secure their IT development and operations using a pragmatic, risk-based approach. The diverse background of our founders allows us to apply security controls to governance, networks, and applications across the enterprise.

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