Software development involves several distinct phases, each with its challenges. Traditionally, developers and operations teams have worked separately, leading to delays, miscommunications, and unhappy customers. Enter DevOps—a cultural and technical approach designed to bridge this gap and streamline the software delivery process.
Understanding the Software Development Lifecycle (SDLC)
The SDLC outlines the steps to develop software systematically, It involves following steps:
Requirement Gathering and Analysis
Define product features and user needs.
Assess market trends and risks.
Planning : Determine costs, resources, and timelines.
Designing : Create design documents as roadmaps for developers.
Development : Developers write code based on design.
Testing : Testers identify and fix software defects.
Deployment: Operations deploy the software for users.
Maintenance: Ensure uptime and adapt to changes.
Different Models in SDLC
Software Development Life Cycle (SDLC) offers various models to guide the development process, each tailored to specific project needs based on factors like cost, risk, and timelines.
1. Waterfall Model
The waterfall model follows a sequential approach where each phase must be completed before the next begins.
Limitations:
Difficult to make changes once the process starts.
Working software is produced only at the end of the lifecycle.
Unsuitable for projects where requirements evolve over time.
Example: Emma, a client, isn’t clear about all her requirements upfront and prefers flexibility during the development process. Hence, the Waterfall model is not a good fit.
2. Agile Model
Agile divides the development process into smaller iterations (2–4 weeks), enabling flexibility and continuous improvement.
/Ky Features:
Deliverables are produced after each iteration.
Feedback from clients like Emma can be incorporated into the next iteration.
Supports evolving requirements.
Agile ensures Emma can observe progress regularly and suggest new ideas throughout development, making it a preferred choice for dynamic projects.
Other Models
Spiral Model: Combines iterative and risk-driven approaches.
Big Bang Model: Focuses on minimal planning and is suited for small projects.
Each model serves as a pathway to achieve the same goal, chosen based on the project’s complexity, flexibility, and risk tolerance.
From Agile to DevOps: Resolving Code Delivery Challenges
In an Agile project, frequent code changes overwhelmed the operations team, leading to deployment failures, delays, and client dissatisfaction. Developers blamed Ops for slow deployments, while Ops struggled with unclear instructions and constant requests, creating a "wall of confusion" between the teams.
Introduced DevOps to resolve the issues. By training both teams on each other's processes, automating tasks like builds, testing, and deployments, and integrating workflows, the delivery pipeline became seamless. Collaboration improved, deadlines were met, and the client was satisfied. DevOps bridged the gap, turning the team into a well-oiled machine.
The fully automated DevOps lifecycle eliminates human intervention, reducing errors and saving time.
What is Continuous Integration ?
Continuous Integration (CI) is a DevOps practice that automates the process of generating software and its features quickly and efficiently.
Developers: In a team setting, developers write numerous lines of code, which are stored in a centralized repository called a version control system, such as GitHub. Developers frequently pull and push code to these repositories, resulting in continuous code changes or commits.
Deployment and Testing: The committed code is transferred to a build server, where it is built, tested, and evaluated. This process generates an output called an artifact—an archive of files produced during the build process, based on the programming language used.
Artifacts are packaged into specific formats like WAR or JAR for Java, DLL/EXE/MSI for Windows, or ZIP/TAR files. These packaged artifacts are stored in a software repository and then deployed to servers for further testing. Once deployed, software testers conduct additional testing, and upon approval, the artifact is ready for production deployment.
Problem: The developers have been working on writing lots of code. After their work is done, the code is sent to the build server, where it's built and tested. But, wait—there are lots of errors, bugs, conflicts, and build failures. Now, the developers must go back and fix these issues, rewriting parts of the code. This rework could have been avoided if the issues had been detected earlier.
The solution to this is simple: after every commit, the code should be automatically built and tested. This way, developers don’t have to wait for a big code collection filled with bugs. However, manually doing builds and releases multiple times a day isn’t feasible. So, the process is automated.
Every time a developer commits new code, the automated process fetches the code, builds and tests it, and sends a notification if there's a failure. Once the developer receives the notification, they fix the issue and commit again. The cycle continues until everything is correct, and once the code passes, it’s versioned and stored in a repository.
This process, known as Continuous Integration (CI), ensures defects are caught early, preventing them from multiplying.
Tools for CD:
Continuous Delivery
Continuous delivery is an automated process that quickly and efficiently delivers code changes to servers at a fast pace.
It's an extension of continuous integration (CI).
CI automates the code build and test process. Whenever developers commit code, it gets automatically built and tested. If everything is good, the resulting artifact is stored in a software repository. CI's goal is to detect defects early so they don’t accumulate.
The ops team then receives requests to deploy the artifacts to servers for further testing. However, deployment often fails, increasing lead time. The development and ops teams must collaborate to resolve these deployment issues. In agile development, code changes happen frequently, and deployment isn't just about moving software to servers. It involves server provisioning, dependency installation, configuration changes, network or firewall adjustments, and more.
As the CI process generates artifacts more rapidly, the ops team faces more frequent deployment requests. After manual deployment, the QA team tests the software and sends feedback, creating more manual processes. To overcome this, automation is essential.
Tools like Ansible, Puppet, Chef (for system automation), Terraform (for cloud infrastructure), Jenkins, and Octopus Deploy (for CI/CD) can automate deployment. Testing, including functional, load, performance, and security testing, should also be automated.
Ops teams write automation code for deployment, while testers automate software testing. All these steps are integrated with the developer's source code.
By automating every step and integrating everything, you achieve continuous delivery.
