The Role of SDLC in Making an Outstanding Product

Effective planning is only half the battle when it comes to delivering a successful project, especially if we’re talking about software development. When every mistake may lead to losses in an already limited budget, you need to be confident in every step your team takes. For one thing, PMI’s Pulse of the Profession survey showed that 37% of projects fail due to a lack of defined goals and milestones to measure their effectiveness.

That’s where the Software Development Lifecycle (SDLC) comes in. If your in-house or outsourcing development team follows SDLC, you can be sure they’re working according to specific tried and tested stages. This helps to build a consistent workflow and produce excellent software in time and within budget.

What is the SDLC and What are the Benefits of SDLC

The Software Development Life Cycle (SDLC) is a systematic process used by software engineers to develop high-quality digital products. This structured approach divides the development process into distinct phases, each with its own set of activities and deliverables. The main phases include requirement analysis, design, coding (development itself), testing, deployment, and maintenance.

The Software Development Life Cycle (SDLC) offers numerous benefits that streamline and enhance the process of software development. Here are some of the key benefits of SDLC:

Improved Quality: By following a well-defined process, the SDLC helps in producing high-quality software. It includes specific stages for testing and quality assurance, ensuring that the final product is robust and meets the desired standards.

Early Identification of Issues: The SDLC model facilitates the early identification of potential issues. This early detection allows for timely interventions, reducing the cost and effort required for corrections at later stages.

Better Risk Management: The structured approach of the SDLC helps in better risk management by outlining potential risks at each phase and providing a framework for addressing them.

Maintenance and Continuous Improvement: Post-deployment, the SDLC framework continues to focus on maintenance and continuous improvement of the software, ensuring it stands the competition over time.

SDLC Stages

Each stage has the demands and goals the team needs to achieve. Let’s take a closer look at them.

Usually, an SDLC consists of seven stages:

1. Planning

First things first: we start by planning the project and agreeing on its scope. This is the stage when you decide on the project plans, schedules, and timelines, and, of course, cost estimation. It might sound obvious, but make sure you have the budget that will cover the entire project, with its risks and possible issues.

2. Analyzing requirements

Before getting down to development, the team needs to know the requirements set by you - the client. This stage lays down the foundation of the entire project and helps make sure the team delivers precisely what the client needs. This is also the time to discuss the functional and system requirements of the project, as well as user requirements and operational ones.

3. Designing the system

When the development team has the client’s input, they turn it into design specifications, which are used to map out the solution that will be developed. Design documents created at this stage will become a perfect guideline for understanding the system architecture at future stages. But don’t expect anything elaborate: at this point, only prototypes and wireframes will do the trick.

4. Developing

With all the preparations and estimations behind, it’s finally time to start coding. Since you’ve prepared the design documents in advance and have a complete vision of the system’s architecture, it should be pretty straightforward. At this stage, the only thing that matters is the development team. Regardless if they’re your in-house employees or a dedicated team, these people should follow the blueprint created during the previous stage to meet your expectations.

5. Testing

Some companies decide to spend less time on software testing and more on product promotion. However, it has been proven time and time again that professional and timely testing is as important as development. So, don’t disregard this step, because this is when you can actually see if the end-product meets the goals set earlier. And make sure you deliver an error-free solution, of course.

6. Deploying

When your software is ready and tested, it’s time to deploy it and allow the first users to participate in beta-tests. This stage is a milestone in creating user-oriented products and your last chance to make significant changes to the software since, after the release, you’ll only be able to add bug fixes and new features.

7. Maintaining

After the release, this stage may be a continuous process for your team. It’s important to carefully monitor the experience users have with your solution and pay attention to their feedback. This will allow you to update the product according to their needs. Besides, maintaining the product also means releasing new features to attract more users - just make sure they fit the goals set at the first SDLC stage.

Following the SDLC turns software development into a less risky step-by-step process and helps deliver high-quality products users will enjoy.

TOP 6 SDLC Models

Different software projects call for different timelines, technologies, and budgets. Thus, when speaking of SDLC methodologies, there’s no one-size-fits-all solution. Each comes with unique features catering to different project requirements. Let’s briefly look at the 6 most popular SDLC models to understand which best applies to your software development.

Agile Model

This iterative and incremental model encourages customer feedback and adapts to changes quickly, making it ideal for projects with dynamic requirements. Agile is one of the most common SDLC models. The idea is to release a product on the ongoing cycle to detect and solve minor bottlenecks before they evolve into major issues.

Waterfall Model

A straightforward, linear approach where each phase is completed before the next begins. Being one of the oldest SDLC methodologies, Waterfall model is best suited for projects with well-defined, unchanging requirements.

V-Model (Validation and Verification model)

This model emphasizes rigorous testing and is often used in projects where quality matters more than time to market delivery. With the V-model, rigorous testing happens at every stage of development to validate that the system meets the needs of the customer and target audience.

Spiral Model

The spiral model combines elements of both iterative and waterfall models. It focuses on performing the four phases of development repeatedly (planning, risk analysis, coding, testing) focusing on defying and mitigating potential risks. It’s well-suited for large, complex projects.

Incremental Model

Following the incremental model, the development team breaks complex software into small, manageable increments, allowing for partial implementation of functionalities. Such an approach allows for identifying flaws early and easily and adapting the software to the ever-changing demands of a customer.

DevOps Model

In DevOps model, operations and development teams work closely throughout the development cycle. DevOps Model emphasizes continuous development, integration, and deployment that happens due to tight communication between the operational and development experts.

AI tools for SDLC

AI tools and platforms are reliable assistance at each stage of project development lifecycle – from initial planning and coding to testing, deployment, and ongoing maintenance. Here are a few examples of the best AI tools for SDLC to consider.

AI Automated Code Generation

GitHub Copilot: Developed by GitHub and OpenAI, Copilot provides code suggestions and auto-completion features to assist developers in writing code more efficiently.
Tabnine: An AI-powered code completion tool that supports various programming languages and integrates with several IDEs to enhance coding productivity.

Predictive Analytics for Risk Assessment

Jira Software with Predictive Analytics: Atlassian’s Jira can integrate AI for predictive analytics to forecast project timelines and potential issues in agile software development.
Monte Carlo Simulations: Tools that use Monte Carlo methods for risk assessment in project management, providing insights on potential delays and cost implications.

Enhanced Testing

Applitools: Uses AI for visual testing and monitoring, allowing for the automated detection of visual flaws in software applications.
Testim: An AI-based platform that simplifies the creation, execution, and maintenance of automated tests, making the process faster and more reliable.

Intelligent Requirement Analysis Tools

ReqSuite RM: This tool uses AI to optimize the management process, ensuring clearer understanding and better documentation of project requirements.
IBM Watson Requirements Quality Assistant: Helps in improving the quality of requirement documents by using AI to identify ambiguities and inconsistencies.

The Bottom Line

A smooth strategy and clear goals are the cornerstones of any successful project. That’s why we advise you to only work with specialists who can take care of planning and breaking the complex development process into easily evaluated stages. At Perfsol, we help our clients set clear goals as well as estimate the cost of each stage and the resources needed to build an outstanding project.

With more than 20 years of experience in software development, we’ve mastered the art of working according to SDLC. So if you’re looking for a professional software outsourcing vendor, contact us anytime.