7 Stages of the Software Development Life Cycle

Phase 1 – Planning

Everything starts with a plan. The planning phase is the foundation the whole project will be built on. Here the team defines the scope, goals, initial requirements, and needed resources. It’s also the time to identify stakeholders, budgets, and deadlines.

Most important: this is when you spot risks. Teams that skip this step usually end up facing surprises that could have been avoided with a bit more alignment.

Phase 2 – Requirements Analysis

In this phase, the team focuses on understanding what really needs to be built. The goal is to capture the end user’s needs and turn them into clear functional and technical requirements.

In startups, it’s common to skip this phase to speed up delivery. The problem is it comes back as rework. A simple practice is to use short user stories validated with real customers before writing any code. Lightweight documentation (like Notion or markdown in the repo) is enough to keep the whole team aligned.

Phase 3 – Design

With the requirements set, it’s time to plan the architecture. This includes:

Folder and component structure
Data modeling
Choosing technologies and frameworks
User experience design (UI/UX)

You don’t need long documents. But setting clear boundaries for each module and standardizing flows helps avoid rework and makes the system easier to scale. Quick technical reviews, even just between two devs, already help keep the architecture under control.

Phase 4 – Implementation

This is where the project comes to life. Developers turn requirements and designs into code. It’s essential to keep good version control, make small commits, and ensure every piece fits well with the rest of the application.

During this phase, unit tests should be included from the start. It’s also when code review begins to directly impact the pace and quality of delivery. It’s worth paying close attention.

At this stage, code review can become a bottleneck

Code review is an important part of the development cycle, but when it’s poorly structured, it can hurt the team’s efficiency. In many startups, it ends up being delayed or done superficially, which directly impacts the next phases.

During implementation, for example, it’s common for pull requests to wait too long for approval or get merged without proper review. These delays slow down delivery and can let problems slip through unnoticed.

In practice, this can lead to code with inconsistent standards, low readability, or bugs that only show up in testing or even in production. On top of that, lacking proper review makes future maintenance harder.

If the team doesn’t have a structured process yet, it’s worth starting with the basics: good code review practices help ensure the process is efficient, focused, and actually adds technical value. Having a review checklist also helps standardize criteria, makes the reviewers’ job easier, and prevents important points from being missed.

According to GitLab’s research, delays in code reviews are among the main factors slowing down delivery speed in development teams. In small teams, this tends to happen even more since the same developers take on multiple responsibilities.

In this context, using artificial intelligence can help make the review process faster and more reliable. There are already tools that act as automated reviewers, analyzing pull requests and pointing out issues clearly, without depending on someone on the team being available.

At Kodus, we built Kody exactly for this.

It’s a code review agent that runs automatically on PRs and delivers clear comments focused on readability, security, and the team’s technical standards. Instead of generic feedback, Kody tries to replicate the perspective of an experienced team member, without slowing down the flow.

This helps reduce manual effort, avoid bottlenecks, and keep the delivery pace steady even when the team is stretched thin.

Phase 5 – Testing

After implementation, the software goes through testing. The focus is to make sure everything works as expected and catch problems before users do. There are different kinds of tests you can run, like unit tests, integration tests, user acceptance tests, and performance tests.

Even if the team doesn’t have dedicated QAs, it’s essential to test the main flows. Start with automated unit and integration tests. If possible, add manual tests for key use cases. The important thing is to make sure no obvious bugs slip through.

Phase 6 – Deployment

Once the system is approved, it’s time to put it into production. Automating this process from the start, even with simple scripts, helps avoid human errors and builds confidence in the team.

Practices like feature flags, gradual rollouts, and automated rollbacks increase delivery safety, even in small teams. The goal is for deployment to be a predictable step, not a stressful moment.

Phase 7 – Maintenance and Evolution

After launch, the work doesn’t stop. Bugs can show up, requirements can change, and new features need to be added.

This phase requires constant attention to user feedback, usage metrics, and production issues. Automating monitoring and keeping review processes active help prevent the codebase from deteriorating over time. Maintenance isn’t just putting out fires — it’s part of the product’s evolution.

Conclusion on the Software Development Life Cycle

Applying the SDLC in startups doesn’t mean following heavy processes. But ignoring critical steps usually comes at a high cost.

Startups that follow even 50% of good life cycle practices are already ahead of most. Start small: standardize code reviews, test everything critical, and don’t postpone refactoring. With tools that automate part of the process, like Kodus, it’s possible to balance speed and quality from the start.

Liked this content? You can keep learning with:

The biggest problems with the SDLC in startups