Automate your Software Development Life Cycle (SDLC) by using a repetitive development processes to gain additional productivity and improve quality and costs.

Development teams are increasingly under pressure to release applications quickly; reduce development, operations, and maintenance costs; and remain relevant to business objectives.

Using a purely manual software development process can be useful when starting out and processes are not yet concrete. At a certain point, you will hit scalability limits around traceability and throughput. This will affect overall product quality.

Some examples of this issue occur in the testing domain:

  • Regression tests will increase over time as applications become larger and more complex, causing testing capacities to reach an upper bound, slowing testing throughout. The urgency of release to market means a product may not be fully regression tested.
  • Functional tests that are repetitive will consume significant time during test cycles and will grow linearly with the number of new functional tests. As with regression testing, pressure to release could cause deployment of a product that is not yet fully tested for end use.
Automation DevOps Tooling - CLM Consulting Inc.

Automation DevOps Tooling – CLM Consulting Inc.

DevOps tools

The diagram below is a generic logical flow where everything gets automated for seamless delivery:

  1. Developers develop the code and this source code is managed by Version Control System tools like Git etc.
  2. Developers send this code to the Git repository and any changes made in the code is committed to this Repository.
  3. Jenkins pulls this code from the repository using the Git plugin and build it using tools like Ant, Maven or Gradle.
  4. Configuration management tools like Ansible deploys & provisions testing environment and then Jenkins releases this code on the test environment on which testing is done using tools like selenium.
  5. Once the code is tested, Jenkins send it for deployment on the production server.
  6. After deployment It is continuously monitored by tools like Nagios.
  7. Docker containers provides testing environment to test the build features.

 

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