What is Continuous Integration (CI)?

Continuous integration (CI) is the first phase in an automated software release pipeline. CI allows developers to build, test, and merge code changes to an application in a central repository. It helps ensure that disparate app development processes aren’t conflicting or overlapping. With CI, a developer practices integrating the code changes continuously into the main software branch along with changes from the rest of the team. 

What is the state of continuous integration in software?

Application development is becoming increasingly important for all areas of business—from new releases to updates and maintenance of existing apps. As a result, many are adopting modern practices—including agile development—to quickly build, test, and release apps that are secure, reliable, and scalable. 

In most cases, modern software development involves multiple developers working on different parts of an application independently–often called branches. But when it comes time to combine the work of multiple developers, problems can arise if one developer’s changes conflict with another’s updates. Merging these different branches into the shared repository (called the trunk) is a manual, time-consuming process—particularly if developers have separate integrated development environments (IDEs) instead of one, cloud-based IDE.

Continuous integration leverages automation to make it easier to merge each developer's changes regularly (often, daily) back to the shared repository. When a developer’s changes are merged back to the trunk, they are validated through automated integration tests. These will surface any potential problem areas, including conflicts with other devs, making it easier to fix bugs quickly.

What are common hurdles to implementing CI?

When introducing CI to a team, it’s almost impossible to implement it all at once. Instead, organizations should start small: rolling it out to individual projects or teams. Where possible, introduce a CI workflow at the start of the project, since transitioning existing projects can be time-consuming. Often, CI is rolled out to teams that are too large to effectively manage the process.

While CI is designed to automate testing, it can be difficult to ensure full accuracy of the entire testing process. If tests are flawed (whether because they’re poorly written or not a fit for the application in question), it can create problems in production. Security is also a critical component of automated testing—and often overlooked by teams in the process of implementing CI/CD.

To mitigate some of these challenges and measure success, track efforts as CI is implemented as part of DevOps to see how much CI is shortening development timelines—or where challenges are arising. 

A few helpful areas to monitor: 

  • Change lead time: How long does it take to run your entire workflow? CI usually takes this process from weeks or months down to hours or minutes.
  • Deployment frequency: How much work is moving through the development pipeline? With CI, automation helps ensure that even the most complicated updates are deployed quickly and easily.
  • Change fail percentage: Simply, how many changes to the primary code base result in failures? 
  • Mean time to recovery (MTTR): How long does it take to recover from an incident if one does arise?

What is the main purpose of continuous integration in application development?

CI makes it faster and easier for enterprise software teams to design, automatically test, and iterate on different components of a larger application development project—without wasting time fixing complex challenges that arise when merging disparate branches of development. Overall, this process can drive a host of benefits for your software organization.

Empower Developers and Teams—at Scale

CI improves agile development workflows, removing a great deal of communication and coordination among dev team members. This allows developers to work on their own projects independently, without worrying about whether their new code will integrate with the rest of the codebase. In addition to giving individual developers more autonomy, CI also drives accountability, visibility, and communication among the dev team, making collaboration faster and easier. 

Finally, with less communication overhead and more seamless operations among teams, companies can more effectively grow their engineering teams as well as the size of their codebase.

Faster Iterations, Less Risk

With CI’s automated testing, dev teams can detect bugs and defects in a codebase earlier—making them easier (and cheaper) to fix and reducing the risk involved in any project significantly. And with faster deployment cycles, each developer’s work is closely tied to the application in the production environment. As a result, teams working with CI tend to work on smaller, more incremental changes when developing new features.

Enhanced Your Feedback Loop

By working in smaller batch sizes, software teams have faster feedback on these small updates—making it easier for them to test and iterate new features, measure their success, and address bugs rapidly. This feedback is also incredibly valuable for product managers, who will have access to more data and insights about the product’s direction as well as monitor the progress of a development project. 

The Missing Ingredient in Continuous Integration: Programmable Data

Over the last two decades, storage, compute, and code have all been automated, giving rise to the cloud and fast continuous integration/continuous delivery releases. While companies have mastered deploying code within their pipelines at faster speeds, the speed of delivering data into test environments rarely matches the pace of the release train. Data is heavy, complex, and filled with risk. The inability to quickly deliver test data and streamline QA processes can impede an organization’s ability to take new features and functionality out to market quickly. 

Accessing and refreshing data across disparate test environments typically requires database administrators, storage specialists, and network administrators—who often delay test data requests. As a result, QA teams make do with what they have, which include stale, partial, or synthetic data, creating mistakes and errors that lead to systems breaking down, more defects, and higher costs associated with fixing errors. Valuable development and testing time are wasted orchestrating data rather than creating and releasing high quality code. 

To achieve Internet, cloud speed and scale, everything—including data—has to be programmable and API-driven. With a programmable data infrastructure, enterprises can automate a range of complex data operations, including compliance with privacy regulations such as the GDPR and CCPA. At the end of the day, environments are useless without the right data.