In today’s fast-paced software development landscape, automated testing has become an indispensable tool for ensuring quality, efficiency, and reliability. However, the implementation of automated tests is not a one-size-fits-all approach. The way automated testing is integrated can vary significantly depending on the software development lifecycle (SDLC) being used.
This explore how automated testing fits into different SDLCs, examining the specific strategies, tools, and practices that make automation effective in each context. We’ll compare the advantages and challenges of incorporating automated tests in Waterfall, V-model, and Agile. By the end, you’ll have a clearer understanding of how to tailor your automated testing approach to align with the needs of your development process, no matter which SDLC you follow.
Waterfall
Waterfall is a traditional, linear approach to software development typically adopted before the emergence of Agile. In this methodology, tasks are completed one after another (like the flow of a waterfall), moving through distinct phases such as requirements, design, implementation, verification, and maintenance. Each phase must be completed before the next begins. This approach prioritizes structure, thorough planning, and comprehensive documentation, which makes it well-suited for projects with strict deadlines and clearly defined outcomes. It works perfectly for governments and regulatory projects or embedded systems. In Waterfall model, the implementation of test automation typically happens during or after the implementation phase. Tests are usually executed during the verification phase, since software components are not ready for testing until then. Since automation comes late, defects are often found in later stages and they are costly to fix because they require major rework. A best practice is to begin preparing automated test scripts during development, so they are ready when the system reaches the testing phase.
V-model
V-model is another sequential SDLC model, but unlike Waterfall, it directly pairs each development phase with a corresponding testing phase, forming a “V” shape when visualized. For example, the definition of high-level requirements coincides with the creation of high-level test plans. The V-model aligns with traditional test levels, such as component testing, system testing, and acceptance testing. Implementing a test automation framework (TAF) at each level is not only feasible but also highly recommended, as it helps validate every development phase, catch defects earlier, and confirm that each piece of the software works as intended. This structured alignment also improves traceability, as each requirement has a corresponding automated test, making it easier to verify coverage and identify gaps. Since test activities are planned in parallel with development phases, the V-model naturally supports shift-left testing, ensuring that the test coverage is considered early in the lifecycle. A common challenge in the V-model is that heavy upfront planning can make it difficult to adapt if requirements change frequently, which is why it is less suitable for highly dynamic projects.
Agile
Agile software development shifts the focus to flexibility, adaptability, collaboration, and continuous delivery. It splits development into short iterations called sprints, allowing for frequent feedback, continuous improvement, and quick adaptation to changing requirements. In this model, testing is not a subsequent activity but it happens concurrently with development. Test automation is deeply embedded in the development cycle, with teams (including automation engineers and business stakeholders) collaborating closely to define the roadmap, timelines, and delivery of tests. Practices such as code reviews, pair programming, and frequent execution of automated tests are commonplace and support quality at every step. Agile also breaks down silos, promoting collaboration across developers, testers, and other stakeholders to ensure broad test coverage. The main goal is in-sprint automation, enabling continuous validation and rapid feedback within each sprint cycle. However, this can be challenging if user stories are too large or features are unstable when testing starts.
Conclusion
Automated testing is no longer optional in modern software development — it’s an essential practice that ensures speed, quality, and confidence in delivery. Yet, as we’ve seen, the way test automation is applied depends heavily on the chosen SDLC model. In Waterfall, automation often comes late and focuses on regression, while the V-model allows for earlier planning and alignment between requirements and tests. Agile, on the other hand, thrives on in-sprint automation and close collaboration, making test automation a natural part of the cycle.
The key takeaway is that successful test automation isn’t about a single tool or framework — it’s about adapting your strategy to the development model in use. By tailoring automation practices to fit the workflow, teams can maximize coverage, accelerate feedback, and deliver reliable software faster.
https://medium.com/@marta.rakowska91/from-waterfall-to-agile-adapting-automated-testing-to-different-sdlcs-37c5770f9808a>