What makes a software product development process efficient, reliable, and capable of delivering high-quality software products? Those of us in the product development business understand that an automated Software Development Lifecycle (SDLC), supported by a robust DevOps strategy, is key to achieving this. This is the foundation of product innovation as it breaks down organizational silos to streamline processes, promotes collaboration, and ensures automation and continuous improvement. But there are some prerequisites or essential components that must be addressed before SDLC can be automated. It is critical to understand each of them in depth before embarking on the development journey. Let’s dive in.
- Automating Requirement Management
As we know, the software development process begins with requirements and how they are captured and managed within the system plays a crucial role in determining the efficiency of subsequent processes. There must be a central document repository where all requirements are collated and saved. This single source of truth ensures that all team members have equal access to the data, know what needs to be done, and are aligned with each member’s responsibilities.
Automation for ensuring traceability across the development process can only begin once the requirements have been captured. It makes it easy for teams to track progress and map every step to the requirements to ensure that they are being met correctly. Scripts and configurations on platforms like JIRA help to automatically move tasks through different stages of development with little to no manual intervention.
An automated SDLC offers developers the ability to control the process of changing requirements. This is extremely important to ensure that the scope of work does not increase without any checks, which can impact timelines, quality of the product, and even budgets. When using agile methodologies, this control ensures that changes from one cycle to another or sprint to another are managed effectively. It also ensures that each iteration is focused and in tune with the requirements.
- Automating Source Code Management (SCM)
Effectively automating and managing source code is the second step and an important component of automating SDLC. It ensures continuous integration that is crucial for maintaining code quality, helps manage multiple product versions, and delivers efficient and reliable software releases. SCM tools like GitLab and BitBucket help teams to accurately track the version of the code being worked on. This is critical in environments where multiple releases must be managed simultaneously – different products for different customers or even different versions of the same product. These tools also allow developers to work on multiple versions or features of the code independently in a process termed branching. Multiple branches are merged into the main codebase they are completed. This helps streamline processes when multiple releases are being worked on simultaneously.
Many SCM tools also integrate with AI tools that help to automatically enforce coding quality. This means that all code must meet pre-defined criteria before they can be checked in. In fact, SCM is inextricably tied in with code quality as it ensures that only code that meets defined standards are integrated into the main codebase. Here, it is important to note, that while automation is key to efficient SDLC and product development, the early stages of the process must leave room for manual code review. This reliance on manual reviews is likely to reduce as processes mature.
- Ensuring Continuous Integration (CI)
After the code is committed to the repository it is not in the form of a usable software. And this is where continuous integration comes in. This involves integrating code changes into the main codebase and automatically building and testing the software to ensure it works as intended. At the CI stage, code is collated from different developers working on different components and integrated into a controlled environment to make a comprehensive and functional software product. There are two critical components of CI – automated build and automated testing.
- Automated Build: Automating build processes allow faster and more reliable delivery. Automated builds are important especially where there are active dependencies to be managed. This means that every time code is committed, the build process is automatically triggered, and any issues are detected early in the development cycle.
- Automated Testing: Automated testing is a crucial driver of continuous integration. It must be conducted across levels. A continuous testing pipeline ensures that tests are run automatically every time new code is integrated, providing immediate feedback to developers.
The different kinds of automated testing include:
- Unit Testing: Each component of the software is tested to ensure that they work as intended independently.
- Component Testing: Slightly larger pieces of code are tested to check if larger sections of the software work correctly.
- Integration Testing: This tests if different components of the software work correctly together. Automating this is usually challenging as complex aspects of the software must be tested. For example, the process must determine how different components interact with each other including API handshakes and data flows.
- Regression Testing: This ensures that new code changes do not affect existing functionality. Automating this is important to maintain stability as the software evolves. This is also a challenging process to automate because testing interactions between different components is a complex task.
CI is a crucial component of automating the SDLC process and the best way to describe it is by comparing it to a train journey. Each passenger is a component of the software. Each passenger boards the train after having been checked for individual capabilities – or unit tested in software terms. As the train continues its journey, more passengers are added, and they start interacting with each other, which might reveal issues that were not detected during the individual tests. This is where continuous testing comes in. Just like the conductor of the train, continuous testing ensures that the train runs smoothly by monitoring how new components are integrated and running tests whenever a new one is added. As the train picks up speed, or the process matures, integration and testing happen in parallel, ensuring real-time detection and resolution of issues.
- Ensuring Continuous Deployment
Continuous Deployment is largely a result of the other three factors discussed above, but there are few key facets that need to be in place to facilitate continuous deployment. The most critical aspect to continuous deployment is having an automated deployment pipeline in place. And there are three key components of the continuous deployment process.
To begin with, continuous deployment also calls for effective deployment tools that automate repetitive and manual deployment tasks, reducing human errors and ensuring that deployments are consistent across environments (e.g., staging, production). Here’s a short list of the functionalities that these tools offer:
- By standardizing deployment steps, these tools create a reliable, repeatable process that allow teams to deliver code quickly and with confidence. Tools like Jenkins, GitLab CI/CD, and Octopus Deploy can run automated scripts that follow the exact same sequence every time, minimizing the chances of deployment errors.
- Deployment tools provide dashboards and logs that ensure visibility into each step of the deployment process, helping teams monitor, troubleshoot, and audit deployments effectively.
- They enable fine-grained control over deployments, allowing for advanced practices like canary releases, blue-green deployments, and feature toggles, so teams can test and deploy changes gradually, reducing risk and gathering feedback in production.
The second component is containerization. This is key in addressing the issue of inconsistency in environments. Containers work by packaging an application along with all its dependencies, libraries, and configurations, ensuring that it behaves the same way in development, testing, and production environments. We want to ensure consistent deployments across different set ups, and this can be ensured by using Docker and orchestration tools like Kubernetes to standardize the deployment environment This consistency eliminates the classic “it works on my machine” problem, reducing deployment errors that arise from differences between local and production environments.
The third component is Infrastructure as Code (IaC). Using IaC tools like Terraform or AWS CloudFormation to define infrastructure makes it easy to replicate environments and automate provisioning. IaC maintains a single source of truth for infrastructure preventing configuration drift and allows teams to confidently replicate infrastructure across multiple environments or regions. IaC configurations can be stored in version control systems like Git. This can provide a clear history of changes, which improves transparency, accountability, and auditability. With IaC, both developers and operations teams can collaborate more effectively, as infrastructure definitions are shared and reviewed like application code. This encourages shared ownership and aligns teams around DevOps practices. IaC fosters a culture of “infrastructure as a software product,” where infrastructure changes undergo the same code review, testing, and validation as application code, improving quality and reliability.
Conclusion
An automated SDLC can deliver higher quality code, make it easier to manage multiple versions and help deliver efficient and reliable software releases. It is important to remember that automating the SDLC does happen in isolation but consists of several individual processes that come together to ensure continuous integration and a fully automated SDLC.
