DevOps at ScienceSoft

ScienceSoft’s DevOps Practices at Each Stage of SDLC

1. Software architecture design

Where possible, we apply modular application architectures to enable and maximize automation across testing, deployment, monitoring, and scaling. Among our first picks are microservices, event-driven architectures, service meshes, and serverless architectures.

2. Technology stack planning

We prioritize using several best-in-class DevOps tools rather than relying on any single platform with its inherent limitations. When creating customized DevOps toolchains, we choose software that aligns with the project's unique technology stack, easily integrates with existing systems and other DevOps components, can be scaled on demand, and is easy to adopt for all project teams.

3. Development, testing, and production infrastructure setup

Instead of manually setting up servers, networks, and other infrastructure components, we use tools like Ansible and Terraform to write code that does it automatically, ensuring consistency and streamlined management of infrastructure resources and configurations. This approach is known as infrastructure as code (IaC). We store infrastructure code in version control and modularize it, which enables flexible automated deployment and configuration of the project infrastructure.

4. Software coding

With clean code, it is significantly easier to automate development processes. At ScienceSoft, we ensure high code quality through peer reviews, static code analysis, adherence to coding standards, and comprehensive unit testing. Our teams also use dependency management tools like npm (JavaScript), Maven (Java), and NuGet (.NET) to maintain stable build environments and minimize compatibility issues between components.

5. Software integration

We establish continuous integration (CI) pipelines to automatically:

• Perform unit tests to enable rapid response to defects.
• Integrate code changes from multiple developers into a shared repository.
• Detect integration issues early by merging changes frequently.
• Compile and build the application from the source code.
• Ensure builds are reproducible and consistent across different environments.
• Analyze code for potential issues such as security vulnerabilities, code smells, or style violations (static code analysis).
• Package build artifacts (e.g., binaries, libraries) for further stages in the pipeline.

6. Software testing

We prioritize automated testing for repetitive tasks (e.g., smoke and regression testing) and high-volume needs (e.g., load and performance testing), ensuring rapid issue detection and resolution. For a project that involved large-scale legacy apps, we leveraged automated end-to-end UI smoke tests, which achieved 56% coverage and were completed in just 7 minutes — a substantial time savings over the 12-hour manual testing process.

We also integrate automated testing into CI/CD pipelines to validate functionality right after code changes.

For both automated and manual testing, we use containerization and virtualization to create reliable, reproducible test environments, reducing inconsistencies and enhancing test reliability.

7. Software deployment

Where feasible, we implement continuous delivery and deployment (CD) pipelines to automate build deployment to staging, testing, and production environments. With end-to-end CI/CD pipelines, ScienceSoft’s teams can integrate, test, and deploy new features in as little as 2–3 hours.

Along with IaC, we use version control systems like Git, TFS, and Apache Subversion to track code changes and enable effective cross-functional collaboration. Our applications are packaged in Docker for uniform runtime environments, while Kubernetes automates scaling and management for efficient operations. To coordinate multi-environment releases, we also leverage tools like Octopus Deploy and Azure Release Pipelines.

8. Software monitoring and management

Instead of deploying a universal monitoring and logging solution across all systems, we often implement targeted monitoring and logging for critical application and infrastructure components and user experience aspects. This approach reduces noise and focuses attention on areas that most impact business operations.

We use performance thresholds and anomaly detection techniques to set up alerts that minimize false positives, ensuring that only relevant issues trigger notifications.

Additionally, we implement automated response scripts for common incidents, significantly reducing the mean time to resolution (MTTR). For example, automatically scaling resources in response to traffic spikes helps maintain system stability and performance without the need for manual intervention. However, we recognize that not all processes should be automated; for example, human intervention is critical for diagnosing the root causes of performance bottlenecks or increased cloud spend.

To further minimize downtime and negative user impact, we adopt ITIL practices, employing tools like ServiceNow to create structured incident response processes, which include clear on-call rotation schedules and escalation policies. We also establish and maintain effective feedback loops from operations back to development to ensure that monitoring insights feed into future releases, improving software quality and aligning development with operational realities.