What is the software development life cycle (SDLC)?

The SDLC breaks down software development into distinct, repeatable, interdependent phases. Each phase of the SDLC has its own objectives and deliverables that guide the next phase. Taken together, the phases of the SDLC form a roadmap that helps development teams create software that meets stakeholder needs, project requirements and customer expectations.

There are various SDLC models, and each approaches the phases of the SDLC in its own way. In some models, such as the waterfall model, the phases are completed sequentially. In other, more iterative processes, such as agile, phases can be worked on in parallel. 

Software development involves the balancing of many factors, such as competing stakeholder needs, resource availability and the larger IT environment into which the software fits. The SDLC provides a framework for managing and aligning these factors, facilitating a more streamlined development process.

The SDLC helps stakeholders estimate project costs and time frames, identify potential challenges and address risk factors early in the development lifecycle. It also helps measure development progress, enhance documentation and transparency and better align software projects with organizational goals.

While different teams might implement SDLC methodology in different ways, practitioners broadly agree that the software development lifecycle has seven key phases.

Each phase of the SDLC comprises distinct tasks and objectives. As a whole, the phases provide a standardized software development roadmap.

The project planning phase establishes the goals and scope of a software development project.

The software development team starts by brainstorming high-level project details. The team might focus on ideas such as the problem or use case the software will solve, who will use it and how the software might interact with other applications and systems.

Developers might also solicit input from other stakeholders, including business analysts, line-of-business managers and internal and external customers. Developers can also use generative AI (gen AI) research and coding tools to help identify requirements or experiment with new product features.

Overall, the planning phase aims to both pinpoint the project’s goals and establish what the project does not need, which helps prevent it from becoming too bloated.

The planning phase often produces an initial software requirement specification (SRS) document. The SRS details the software’s functions, required resources, possible risks and project timeline. 

During the analysis phase, the development team collects and analyzes information on the project’s requirements. Analysis enables the team to advance the work they started in the planning phase, moving from a high-level idea to a practical implementation plan.   

Analysis often involves gathering user requirements, conducting market research and feasibility testing, evaluating prototypes and allocating resources. Stakeholders might share organizational performance data and customer data, insights from past developments, enterprise compliance and cybersecurity requirements and the IT resources available.

Software developers might use generative AI to help process all of this information. For example, generative AI tools might be able to identify trends in user feedback or flag potential compliance issues in proposed features. 

At the end of the analysis phase, project managers and development teams fully understand the scope of the project, its functional and technical specifications and how to organize project tasks and workflows.

The design phase involves defining the project’s architecture. Core steps include outlining the software’s navigation, user interfaces, database design and more.

Software engineers review requirements to determine the best way to build the desired software. They consider how the software fits into an organization’s existing landscape of apps and services, both upstream and downstream, and any other dependencies it has.

The development team might also begin to address cybersecurity concerns during the design phase by using threat modeling exercises to identify potential risks for the software. For example, if identity theft is determined to be a significant risk, the team knows to incorporate strong authentication measures into the design. 

Many new software applications use a microservices architecture, a cloud-native approach in which a single application is composed of many smaller, loosely coupled and independently deployable components or services

To help organize development flow, software developers might use a modular design. Software modules are self-contained units of code that perform a specific function. These modules might be connected to create a larger piece of software. Modular design enables developers to reuse existing modules and work on multiple parts of the software at once, which can reduce bottlenecks.

The design phase often culminates in the creation of an early prototype or multiple prototypes, which can be shown to stakeholders and end users to solicit feedback. Gen AI can potentially help accelerate the creation of prototypes. For example, developers might feed detailed functional designs and requirements into an AI tool and have it return a first draft of the software’s code.

Design phase work is collected in a software design document (SDD), which is passed on to developers as a roadmap to use while coding. 

The coding phase, or development phase, is when the team starts writing the code and building the software, based on the SDD, SRS and other guidelines created during the preceding phases.

These documents help guide software developers in choosing the correct programming language, such as Java^TM or C++, and they help project managers divide the project into smaller, discrete coding tasks.

This phase also involves building any additional systems or interfaces necessary for the software to function properly, such as web pages or application programming interfaces (APIs).

Depending on the SDLC model they follow, some teams might perform code reviews and other tests during the development stage. These tests might help identify bugs and other vulnerabilities earlier in the software development lifecycle.

Some developers now use generative AI to help write code, which can reduce development time. For example, in “vibe coding,” developers describe what they want the software to do in plain text, and the AI tool creates the appropriate code snippets. Developers often take this code as a starting point and refine it further. 

The testing phase begins after the development team has created a functional piece of software. During this phase, the team looks for opportunities to eliminate bugs and enhance the final product.

A quality assurance team might perform unit testing, integration testing, system testing, acceptance testing and other types of testing to make sure that all parts of the software work as intended. These tests help ensure that the software meets user and business requirements and works within the organization’s broader IT environment.

Testers also scrutinize the software for security vulnerabilities, identify when and how they occur and document the findings.

Developers use the test results to fix bugs and implement improvements, and then they send the software back to be tested again.

Software development teams often use both manual and automated software testing methods. AI tools can streamline much of the testing process—for example, by generating test cases and analyzing patterns in test failures to uncover root causes.

Many SDLC models use continuous testing. This approach means that testing is not limited to a single phase of the SDLC. Rather, code is tested throughout the entire software development process.  

In the deployment phase, finely tuned software is deployed to the production environment where users can access it.

The goal of this phase isn’t just to get the software into the hands of users. Developers want to make sure that users understand how to use the new software, and that it can be deployed with minimal disruption to user experience or workflows.

Developers might deploy software in phases—such as a beta release, where a limited group of users tests an early version of the software—before releasing it to the public. This approach enables the team to see how the software works in the real world before it reaches general availability (GA). Software teams might also create manuals, conduct training sessions or offer onsite support for users.

The SDLC doesn’t end when the software is deployed. The maintenance phase entails the postdeployment work that software teams do to help ensure the software’s continued operation: pushing updates and optimizations, making unanticipated changes, testing patches, addressing new use cases and squashing any bugs that users find.

Ongoing maintenance and support are necessary to safeguard the longevity of any piece of software. Think of it like maintaining a house: Over time, small pieces will be misused or break down. They will, in turn, be replaced and hopefully improved.

In some development models, such as DevOps models, the development (Dev) and IT operations (Ops) teams use continuous integration and continuous deployment (CI/CD). Code is continuously added to the code base as it is written, continuously tested and automatically deployed to the production environment. In DevOps, maintenance is an ongoing activity rather than a distinct phase. 

There are many different software development models. Some of the most popular SDLC models are:

• Waterfall
• V-model
• Agile
• Lean
• Iterative
• Spiral
• Big bang
• Rapid application development (RAD)

Choosing the right SDLC model depends on various factors. Are the project requirements clearly defined, or likely to change during the development process? How complex is the project? How experienced is the development team? Answering these questions can help stakeholders choose the most appropriate model for a project.

The waterfall model is a linear and sequential software development model in which one stage is finished before the next is started. It provides a structured, predictable process that works for well-defined, stable projects where stakeholders want to be involved only during the review of major milestones.

This model is not very flexible because it requires the completion of each phase before starting a new one. This might make it difficult and time-consuming to correct work in previous phases after completion.

While the waterfall model is less common today than in the past, it is the basis for many subsequent SDLC models.

The V-model, or V-shaped model, is a variation of the waterfall model and is sometimes called the “verification and validation” model. In the V-model, each phase of the SDLC has its own accompanying testing phase.

Frequent testing helps eliminate bugs early on, but the linear structure makes the V (like the waterfall model) less flexible than other methodologies. However, it does work well for software with stable requirements that requires frequent testing.

The agile model runs on continuous improvement and development cycles—often called “sprints”—in which developers regularly make and release small, incremental changes. It is well suited to projects where clients are willing and able to participate in frequent discussions and reviews of progress.

Agile development is responsive to changing requests or requirements, enabling teams to more easily identify issues during the development process. This responsiveness leads to one of the biggest agile software development benefits: Development teams can deal with problems before they snowball into bigger issues.

Variations on the agile methodology—sometimes known as “frameworks”—define roles in the development team to streamline the process further. Two of the most common agile frameworks are scrum and kanban. (For more information, see “SDLC, agile and scrum.”) 

The lean model applies manufacturing principles and practices to software development to reduce waste at every step of the SDLC.

Lean aims to continuously improve business processes during development. Teams regularly sets short-term goals with high standards for quality assurance at every step of development.

To cut bloat and speed up the process, lean prioritizes iteration and faster feedback loops. The model removes bureaucratic processes for decision-making and delays the implementation of decisions until accurate data is available.

In the iterative model, an initial version of the software—or minimum viable product (MVP)—is created quickly and then improved rapidly with successive versions. The model focuses on starting with a small goal and then building software outward from there.

In the spiral model, four phases—determining objectives, resource and risk analysis, development and testing and planning for the next iteration—occur in a repeating cycle, hence the “spiral” name.

With the regular repetition of these four phases, there are multiple opportunities for correction, making this model ideal for high-risk or complex projects where frequent changes are expected. 

The big bang is an informal and unstructured form of software development that lacks the rigorous definition of models normally associated with the SDLC.

Like the theory of the big bang, this model starts from nothing—no planning or requirement analysis. It is considered high risk, but the big bang model can work well for small projects where the parameters are self-explanatory, making detailed planning and management unnecessary. Instead, big bang relies on tester and user feedback for ad hoc software updates during development.

As the name of the model suggests, rapid application development (RAD) relies on fast prototyping and user feedback rather than a long planning period. This structure enables the RAD team to quickly adapt to new user needs and requests.

While similar to big bang software development, RAD tends to track progress more regularly and provides regular opportunities for user and client input. This additional structure makes RAD feasible for larger and more complex projects. 

DevOps is a software development methodology that combines and automates the work of both software development and IT operations teams. The DevOps lifecycle has its own steps, which are similar to the steps of the SDLC. But DevOps reconfigures the steps of the SDLC to create a continuous cycle for software development and improvement.

The core principles of a DevOps approach are collaboration, automation and continuous integration and continuous delivery (CI/CD). Because DevOps addresses the full software development process, it might be considered a software development lifecycle in its own right. 

But DevOps is also larger than this, encompassing a cultural and organizational shift toward shared responsibility and collaboration. Crucially, DevOps is not a single model, but a combination of practices, tools and cultural philosophies.

DevOps addresses the rigidity of the SDLC by making each phase of the software development process continuous throughout the project. Instead of being limited to discrete steps, planning, coding, testing, deploying, maintaining and monitoring all continue throughout a product’s lifecycle. The result is a continuous delivery pipeline in which software is improved through frequent updates.

DevSecOps, sometimes called “secure DevOps,” integrates automated security testing and security practices into the DevOps model. Where traditional software development treats security testing as its own phase, DevSecOps incorporates security considerations into every phase of the SDLC.

By threading security tests such as code reviews and penetration testing throughout the development lifecycle, teams can avoid some delays arising from factors such as vulnerabilities that are identified late in the process. They can address risk management issues earlier, create more secure programs, accelerate vulnerability remediation and deliver more cost-effective software. 

The agile model is one of the most popular SDLC models because it emphasizes collaboration, continuous delivery and customer feedback. This iterative methodology breaks large projects down into time-boxed “sprints”—smaller tasks with discrete goals meant to be completed in short time frames. The idea is to keep the team feature-driven during the development process and enable teams to quickly identify problems and respond to changing user requirements.

Scrum is an agile project management framework that some development teams apply to their software development process. Its name comes from the sport of rugby. In rugby, a scrummage is a way to restart play after possession of the ball has been lost that relies on clear communication between players working in unison. In the agile framework, scrum asks team members to act as a cohesive unit that prioritizes teamwork and open collaboration.

In the scrum framework, development teams are broken down into smaller units, each led by a “scrum leader.” The scrum leader reports to the product owner, who also acts as the point of contact between the scrum teams. Each small team takes full ownership over its assigned task in each sprint. This ownership empowers the scrum team to be adaptable and creative without needing to stop and wait for feedback from other stakeholders.

Kanban—meaning “signboard” in Japanese—is another common agile framework. While scrum works in time-boxed periods, kanban has a continuous workflow. All required tasks are shown visually on a kanban board where all team members can see the work still to be done and prioritize their next steps. The board makes it easier for team members to move immediately to the next step as each task is delivered.

The SDLC provides development teams with a standardized structure and repeatable process, making it easier to consistently create high-quality software. The benefits of the SDLC include:

• Better software
• Improved productivity
• Minimized risk
• Improved project transparency
• Greater collaboration
• More efficient resource management
• Improved customer satisfaction

The SDLC provides a map that helps teams complete complex software development projects within scheduled time frames and cost estimates. In addition, it emphasizes testing and quality assurance as part of the process, increasing overall product and code quality. 

The structure of the SDLC helps streamline projects and eliminate guesswork. With clear documentation to guide progress between phases, the SDLC might reduce software production time and boost development productivity. 

The SDLC can help organizations anticipate and manage project risk. In some SDLC models, risk assessment is performed continuously throughout the development process. Development teams can identify and mitigate risks earlier in the software development lifecycle, before small problems can grow into bigger ones.

The SDLC promotes transparency through standardized documentation and open lines of communication.

Most SDLC models have defined processes for informing stakeholders of what’s already been accomplished, what needs to be accomplished and what their own personal responsibilities are. Armed with this knowledge, stakeholders can understand the work that has come before them and more effectively complete their own tasks.

The transparency of the SDLC might also promote greater collaboration. Stakeholders might align on and openly communicate about goals and pain points. In some models and methodologies, team members are encouraged to form small, highly collaborative groups to find creative solutions to development problems.

Estimating the overall costs of development is an important part of the SDLC process. Stakeholders understand the resources required to complete the project before development begins. Planning for resource requirements ahead of time might help decrease bloat and keep projects on task and on budget.

The SDLC acts as a roadmap for rigorously planning, building and testing software. This roadmap enables a more focused development lifecycle, which can help reduce feature bloat, make software easier to use and help ensure software fits into an organization’s existing IT landscape.

Software that has been thoroughly tested should also have fewer bugs when deployed.

Here are a few of the challenges that might complicate or even endanger the successful completion of SDLC projects.

• Staying within the project scope
• Poorly defined requirements
• Determining the optimal level of testing
• Securing software updates and maintenance

Scope creep—when a project’s requirements expand beyond the initial plan—can cause software development teams to blow through budgets and expend extra efforts for little true benefit. Often, these added requirements do not serve the core purpose of the software and might even derail the optimum direction of development.

A ceaseless drive toward perfection can also warp a project’s scope. Some highly sensitive software applications might need to be nearly perfect, but for most software development lifecycles, the perfect is the enemy of the good. A functional, if not perfect, release can get to market more quickly, and it can be improved through iterative rounds of postdeployment maintenance. 

If a team fails to thoroughly analyze and understand project requirements up front, it might go through many wasted work cycles before discovering the software’s actual needs. This can significantly delay release and drive up project costs. 

Software testing can account for almost 33% of system development costs. To speed output and cut costs, organizations might limit testing—and pay the price later, when unidentified bugs and performance issues cause significant problems for end users.

The opposite can also be a problem: Putting software through more testing than necessary before launch. Aiming to eradicate all bugs, development teams might end up delaying a software release with multiple rounds of extraneous testing. 

According to the IBM® X-Force® Threat Intelligence Index, supply chain attacks—in which cybercriminals strategically target third-party vendors to impact multiple organizations—are on the rise.

One common attack vector in software vendors is hijacking the software update process to spread malware instead of a legitimate update. For example, in 2020, the software vendor SolarWinds was hacked and malicious actors distributed malware to its customers under the guise of a software update. The malware allowed access to the sensitive data of various US government agencies by using SolarWinds' services, including the Treasury, Justice and State Departments.

Development teams must take application security measures into account during postdeployment maintenance and updates. In the wrong hands, these processes can become devastating weapons.

The Institute of Electrical and Electronics Engineers (IEEE) reports that 35% of organizations across industries are using artificial intelligence (AI) to aid or accelerate software development. However, incorporating AI into the SDLC can also pose some challenges.

Many development teams are integrating generative AI tools into all stages of the SDLC to achieve more than just simple automation. For example, gen AI coding tools can create software prototypes, generate reusable code snippets and help developers refine their own code. They can also help flag and explain errors in code, and analyze test data to identify trends and patterns in software performance and failures.

Yet with all of the promise of AI tools, they are not without their risks. AI tools might make mistakes and write unoptimized code. If developers do not carefully review all code generated by AI tools, these tools can introduce costly software bugs that are not discovered until much later in the lifecycle. 

Balancing quality and speed might also be an issue. Developers might write code much more quickly with AI tools, potentially speeding up the SDLC. However, ensuring the quality of these outputs might require significant human oversight and validation, which can potentially reverse those time savings. The dilemma here is finding the right balance between the speed of AI and maintaining high standards for software quality.