Exploring the DevOps Lifecycle in Custom Software Projects

The DevOps methodology has revolutionized the world of software development and delivery by concentrating on communication, collaboration, and integration between software developers and IT operations professionals. This culture and set of practices translate to increased deployment frequency and quicker resolutions as a result of improved delivery of high-quality software.

Although DevOps started with web-scale and product companies such as Amazon, Netflix, and Facebook, today it is also being used by organizations that develop custom business software. Custom software is developed in order to meet a client’s specific business needs rather than off-the-shelf packaged software. Its development requires continuous collaboration with stakeholders.

Adoption of DevOps will help custom software teams to increase the velocity of deployments, support continuous delivery, and minimize the risk of releasing updates. It unites development and operations to reduce feedback loops and loop faster.

Let’s see what a DevOps lifecycle for custom software projects has and how it affects the different stages from planning to monitoring.

‍

Planning and Coding

The planning phase sets the foundation for the entire project when building custom software. Requirements are gathered through workshops with stakeholders to outline features and functionality. The product roadmap is defined by the custom software development vendor along with user stories, which help break down work items for the development team.

Rather than completing months of upfront design, custom DevOps teams focus on a minimum viable product (MVP) that has just enough features to be usable and testable. Additional functionality is added incrementally in agile sprints.

With continuous planning, the backlog is constantly reprioritized and groomed. As new user stories are completed, additional ones are introduced based on changing business needs and customer feedback.

The coding or development phase translates requirements into working software. Custom DevOps teams utilize practices like test-driven development (TDD), where engineers first write automated tests based on acceptance criteria before writing application code. This ensures software meets business needs and has baseline test coverage.

Developers create modular components that can be easily maintained and updated independently without impacting the entire system. Refactoring existing code is built into each sprint to prevent the accumulation of technical debt over time. Code reviews encourage knowledge sharing and verify code quality before merging features.

By adopting agile engineering practices, custom DevOps teams can build higher-quality software able to evolve based on changing requirements.

‍

Building and Testing

The build phase takes source code and compiles, links, and packages it into software deliverables. Automated build tools like Maven, Gradle, and MSBuild are configured to perform these steps in the pipeline stage, along with running unit and integration tests.

Custom software executes builds much more frequently than traditional projects. Every code commit triggers the pipeline rather than waiting for nightly schedules. This provides rapid feedback to developers if any issues occur.

Testing proves that the application satisfies business and technical requirements. Custom DevOps teams use test automation frameworks such as Selenium, Cucumber, and JUnit to develop regression test suites, which can be run repeatedly as part of the pipeline. Automated tests perform functional, integration, performance, and security validation.

Test data management poses challenges for custom software that needs to work with copies of production data sets. Data masking and synthetic data generation help provide test coverage without exposing sensitive information.

Shift-left testing principles require that testing be integrated in early stages rather than just before release. By front-loading testing efforts, defects can be fixed faster and cheaper.

‍

Releasing and Deploying

The release phase guarantees the delivery of prepared, built, and tested software to customers. Such release management tools include Octopus Deploy, and CloudBees that take care of versioning, sequencing the upgrade steps, rolling back a failed release, and auditing/reporting.

Custom software projects typically have several staging environments for testing prior to promoting builds into production. Environments are made the same using infrastructure-as-code methods to eliminate inconsistencies. Containers through Docker aid in packaging the application, configs, and dependencies to run the same way in different environments.

Zero-downtime deployments are implemented using blue-green, canary, or feature toggle methods. This eliminates maintenance windows and avoids downtime for users. Automated health checks verify production readiness before routing traffic to new versions.

Progressive delivery techniques for on-premise custom software deliver updates to a percentage of users first, gradually. Any problems affect a subset and not all users. Programmers enable quick rollback if anomalies arise.

Custom DevOps teams can make frequent and safe updates by integrating release and deployment best practices. Fix-forward processes assist in quickly rectifying the production defects once identified.

‍

Operating and Monitoring

The operation phase deals with runtime issues when the software is live. IT ops teams deal with patch management, capacity planning, storage allocation, networking, security, and compliance.

Observability is a focus area. Logging, metrics, and tracing are gathered in order to gain visibility of the health, performance, and errors of a system. Open-source tools such as Prometheus, Grafana, Elasticsearch, and Kibana collect monitoring data through dashboards.

Issues are identified through alerts and triggers automatically sent to on-call resources. Teams utilize runbooks and playbooks to facilitate rapid diagnosis and recovery steps, minimizing downtime. Post-mortems drive root cause analysis and corrective actions.

Chaos engineering includes such experiments as throttling the usage of CPU, terminating cloud instances, or filling up disk space. The goal is to find out weaknesses and improve the system’s capability of withstanding failures. The game day model is followed by custom software teams who run simulated crisis scenarios and evaluate responses.

Effective IT operations processes help achieve service level objectives (SLOs) and maintain uptime goals through proactive and reactive means.

‍

Closing the Loop

The last step in the DevOps lifecycle is feeding the insights back to the earlier phases. Telemetry data reveals insights into the technical debt, performance enhancements, usage of features, and real user workflows.

This provides planning for future releases and sprint prioritization. Software configuration changes can resolve problems related to operations, such as the scaling of resources to level with demand or changing alerts according to noise. Code improvements fix bugs and flaws in the architecture.

By closing the feedback loop across teams and systems, the continuous evolution and improvement of custom software is supported through data-driven decisions.

‍

Benefits of DevOps for Custom Software

Research by the Accelerate State of DevOps report shows that high-performing IT teams who adopt DevOps practices are able to:

• Deploy code over 200 times more frequently than their peers - hourly versus weeks or months
• Recover from incidents 2,604 times faster - minutes rather than hours or days
• Achieve lead time from commit to deploy that is 208 times faster - hours rather than weeks or months

Additional benefits realized for custom software projects include:

Improved Quality

Defects are significantly reduced through test automation, infrastructure as code, and shift-left testing strategy. Bugs detected in production can be fixed rapidly via automated deployments rather than large batched updates. Overall application stability increases through the software development lifecycle.

Reduced Risk

Small and incremental changes reduce the blast radius compared to big bang releases. Progressive delivery techniques provide fine-grained control over the pace of upgrades. Easy rollbacks enable quick remediation of problematic changes.

Better Customer Experience

Continuous delivery means new features, updates, and fixes are delivered to users much faster than if they had to wait months. Increased change velocity keeps up with changing business needs. SRE practices ensure that system outages and performance issues are reduced.

Higher Productivity

Automation enhances human effort on creative software design as opposed to repetitive manual work. Efficient feedback between teams eliminates bottlenecks. Less rework from defects results in faster value delivery at the end.

Enhanced Security

Security testing is implemented early using infrastructure as code, static code analysis, dynamic scanning, and RASP. Policy as code is the codification of controls. Runtime protections are enabled to identify and react to threats.

Optimized Costs

Consuming infrastructure as code, automation maximizes resource utilization and avoids overprovisioning. Containers and microservices prevent monolith sprawl. Technical debt is addressed through paying down interest via continuous refactoring.

‍

Challenges With Adopting DevOps

Moving from traditional delivery models of software to DevOps practices is not without challenges to be addressed if successful outcomes are to be achieved in custom software projects:

Cultural Changes

From siloed teams and processes to shared ownership, open communication, and transparency, an organization needs buy-in, evangelize the benefits through pilots, and motivate people towards collective goals.

Skill Gaps

Although some organizations spend on reskilling initiatives, gaps exist in cloud, containers, automation tools, modern software architecture design, and other areas. The acquisition and retention of such talent is a priority.

Tool Sprawl

The numerous DevOps tools designed for different functions can lead to tool overload. Organizations need to optimize their toolchain – they need to integrate, avoid overlapping capabilities, and standardize on a compatible stack.

Compliance Constraints

Security, privacy, and regulatory standards requirements are still in place. Organizations should incorporate compliance into their workflows through the utilization of policy as code, conducting audits and automating controls rather than viewing compliance as an afterthought.

Legacy Modernization

Remodeling the base of businesses with decade-old custom software developed using old languages, frameworks, and infrastructure is a challenging and expensive task. The strangler pattern of incremental modernization allows for isolation and replacement of parts as opposed to a full rewrite.

The culture shift toward a collaborative and high-trust model is most important, despite the technology and process changes that a company experiences when it adopts DevOps. If led with vision and persistence in overcoming transition obstacles, custom software teams can do well in using DevOps for improved and quicker results.

‍

Conclusion

DevOps has changed custom software development by eliminating silos between development and operations teams. The ongoing lifecycle (planning, coding, deployment, and monitoring) allows organizations to facilitate faster production of high-quality software at a lower level of risk. Despite issues with culture, skills, tooling, compliance, and legacy systems, the benefits are massive: increased quality, decreased risk, increased customer experience, increased productivity, increased security, and optimized costs. With the pressure on businesses to digitally transform ever-growing, teams of custom software have found it necessary to embrace DevOps practices to deliver to the changing business demands more effectively, efficiently, and reliably. The investment rewards in terms of a sustainable competitive advantage.

‍