Back in the 1960s, groups of developers would get together to create blueprints for the most efficient way to manage an information system. This led to the creation of what would eventually become known as the System Development Life Cycle (SDLC).
An SDLC is a project management process that includes planning, building, testing, and establishing the overall structure of how an information system or software product is developed and maintained.
Developing these information systems and software is challenging due to constantly changing requirements, technology upgrades, and increased cross-functional collaboration.
The SDLC methodology provides a phased or systematic management framework that specifies the deliverables at every stage of the development process. Let’s take a closer look at the value of an SDLC and how it works.
The benefits of an Systems Development Life Cycle
While making assumptions at the start of SDLC planning can lead to complications when something unexpected happens, and estimating the overall cost can be tricky, there are mostly only upsides to following an SDLC. These are:
- You’ll gain a clear view of an entire project, the estimated costs and timelines, and the resources and number of employees you’ll need to complete it.
- You’ll be able to define the goals and standards clearly.
- It lowers the cost of software development.
- Because it requires developers to complete each phase before moving on to the next, it helps ensure problems don’t compound.
- It helps developers better understand what they are building and why.
- It accelerates the development process and improves the product quality.
- The iterative process means teams can begin software development without requiring full specifications upfront, allowing systems to be developed faster.
- Because every phase is documented, an SDLC can be re-used for future projects.
The seven stages of Systems Development Life Cycle
Even with the arrival of new technologies, the basic stages of an SDLC remain the same. These are:
- Planning
Developers and other team members outline the system’s objectives and use predictive analysis and AI simulation tools to test an idea’s early-stage validity. This helps project managers visualise the long-term resources required to develop a solution, potential obstacles, and predicted market uptake.
- Analysis
In this stage, a Software Requirement Specification (SRS) document will be created to determine the functional and network requirements. Developers will incorporate data gathered from evaluating existing prototypes, market research, and the goals set for the different stages and overall project.
- Design
The systems architect works on the systems’ design and creates the first prototype of the software to get the overall idea of how it should look. This includes the system interface, databases, core software features, user interface and usability, and the network and its requirements.
- Development
Once the designs are ready, relevant team members start working on the development of the systems, which is where most of the work happens. In this phase, the system’s blueprint moves from model to practical as the developers generate and test the code and bring the design to life.
- Testing
A Quality Assurance (QA) team ensures everything functions properly. This involves assigning bug fixes, executing vulnerability searches, and suggesting improvements through feedback to the developers.
- Implementation
The implementation phase is when most aspects of the programme are brought online. A Canary release (to a limited number of users) may be performed for real-user traffic testing before rolling out to a broader audience. This deployment phase includes several tasks to move the latest build copy to the production environment, such as packaging, installation, and environment configuration.
- Maintenance
Here, the team would fix bugs, resolve customer issues, and manage software system changes. They’ll monitor overall system performance, security, and user experience, and a business systems analyst will monitor usage and performance to report on whether the business goals have been met.
The top SDLC models
Several SDLC models are used in software development, each with its own approach. Because some methods aren’t that flexible, you may need to adopt a hybrid approach, combining elements from various methodologies to create customised SDLC processes. These are the top SDLC models and their key features:
Waterfall Model: This is a linear sequential approach where progress flows steadily downwards through the phases. Each phase must be completed before proceeding to the next, which makes this model suitable for projects with well-defined requirements.
Agile Model: Agile is an iterative approach focusing on flexibility, collaboration, and customer feedback. It breaks down the development process into small iterations called sprints, which makes it suitable for projects where the requirements often change.
Iterative Model: Similar to agile, the iterative model involves repeating development cycles, where each iteration builds upon the previous one. It allows for early delivery of a subset of features and enables refinement and improvement based on stakeholder feedback.
Spiral Model: Combining the elements of both waterfall and iterative approaches, the Spiral model is suitable for projects with high uncertainty and evolving requirements. It involves multiple risk analysis, development, and testing cycles, with each cycle representing a spiral.
V-Model: The V-model (a variation of the waterfall model) emphasises the relationship between each development phase and its corresponding testing phase. Verification and validation run parallel with each development phase, ensuring that requirements are met and defects are identified early.
RAD (Rapid Application Development) Model: This highly iterative, prototyping-based approach focuses on rapid software development and delivery. It involves close collaboration between developers and stakeholders, emphasising quick feedback and adaptation.
DevOps Model: DevOps is a culture and set of practices that emphasise collaboration, automation, and continuous delivery. It involves integrating development and operations teams to streamline software development and deployment while improving overall efficiency and quality.
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