Incorporating continuous testing into DevOps processes provides several benefits to growing enterprises.

Better efficiency and higher-quality deployments: Continuous testing provides an automated method of managing quality assurance and quality interoperation between workflows at each stage of the SDLC.

By integrating continuous feedback loops into user and unit testing modules, developers can receive the actionable insight they need to improve the compatibility and performance of their code before it gets deployed. This efficiency resolves disconnections between multiple DevOps team members and supports accelerated software delivery schedules.

Rapid error discovery and remediation for distributed projects: Today's modern development architectures are multifaceted and multilayered. Continuous testing helps development teams break down these complexities by incorporating a scalable, automated testing solution that significantly improves error discovery and remediation timelines.

Improved user experience: Advanced continuous testing methods can simulate a variety of unique use cases and troubleshooting scenarios and observe how users respond to them. The insight gathered from these simulations enables developers to remove the inefficiencies in the user interface earlier and avoid unwanted surprises after the physical product has been deployed.

Reduced costs due to development-related business disruption: Especially in large interconnected systems, an error in just one module of an application can have ripple effects that can cause unwanted downtime, negatively impacting productivity and the bottom line.

Cloud providers, for example, routinely report breakdowns at one end that paralyze an entire region and cause outages lasting several hours. This can be devastating to organizations that are dependent on high service availability. Continuous testing at a granular level identifies errors that might otherwise be invisible in large software systems and helps to avoid the costs of business disruption.

Continuous testing involves a spectrum of tests that help ensure system reliability, security, operations performance and usability. Tests on the spectrum include the following:

Shift-left testing: This approach prioritizes software and system testing early in the SDLC to help reduce or prevent significant debugging problems down the road.

Shift-right testing: This approach prioritizes testing near the end of the SDLC, with a focus on improving user experience, overall performance, failure tolerance and functions.

Smoke tests: These tests, which can be manual or automated, provide an initial cursory screening for conspicuous flaws in software. While smoke tests are not elaborate in their construction, they still provide a quick and inexpensive solution for the elimination of gross errors in software.

Unit testing: These are ideal for small-scale stress, load, volume or memory leak checks across builds to identify degradations in early developmental stages.

Integration and messaging testing: These check for errors when software modules are working with each other. Continuous testing virtualizes missing dependencies so teams can test how well the end-to-end processes and scenarios perform collectively. The composite code is then compiled and started at run time to test whether they perform as expected.

Performance testing: Testing the performance of application software by itself might not take into account the hardware and middleware in the final production environment. Integrated system testing is required to effectively assess the overall performance of the solution.

Functional testing: This form of testing checks whether the user experience meets expectations and whether functional workflows start as needed across a software system. For example, supply chain software should be able to alert trucks to arrive at factories when inventory is available for shipping.

In contrast, non-functional testing focuses on performance, usability, reliability, response time, load time and scalability. It gauges the readiness of the software to deliver the wanted customer experience.

Regression testing: This testing checks whether there are any changes in performance, functions or dependencies after errors are corrected in any dependent software and that the system performs as before.

User-acceptance testing: Also called application testing or end-user testing, this is when the application is tested in a real-world situation by some subset of intended users. Beta testing is an example of user-acceptance testing.

IT systems and applications run a greater risk of errors because of the following characteristics:

They are increasingly integrated with a host of emerging technologies like cloud computing, Internet of Things (IoT), software-defined networking and augmented reality (AR).

They are increasingly distributed across multiple regions, with a seamlessly interconnected core and edge. Applications for smart cities, autonomous cars and smart utilities are beneficiaries of such an architecture.

In these cases, continuous testing is more demanding because development does not happen in a single location or a company. Third parties, including remote teams, might supply some elements of the system.

The system may be integrated with application programming interfaces (APIs). Each development team operates in different IT environments, including legacy software. The physical environment of every one of the teams is impossible to reproduce for continuous testing.

Fortunately, continuous testing can be virtualized to create a testing environment where the entire system can be virtually reproduced in a single interface. A virtualized environment can be reconfigured with ease to test for a different IT system or for one that has been changed to correct errors.

In a DevOps environment, continuous testing is performed automatically throughout the SDLC and works hand in hand with continuous integration to automatically validate any new code integrated into the application.

Testing tools are preinstalled with testing scripts that run automatically whenever new code is integrated into the application. Typically, the tests start with integration testing and move automatically to system testing, regression testing and user-acceptance testing.

The tests generate data feeds from each application module, and the feeds are analyzed to help ensure that all modules impacted by the new code perform as expected. If a test fails, the code goes back to the development team for correction. It is then reintegrated and the testing cycle starts anew.

Once all tests are passed, the application or project moves to the next stage of the SDLC, typically continuous delivery.

A continuous testing framework is needed for sets of tests to help ensure their consistency across modules in an application, their connectors or APIs and containers, the platforms, their infrastructure and the scenarios that define their requirements.

The set of tests can be sequential like regression tests following unit tests, or they can be concurrent like a new iteration of a module that is accompanied by a test with corresponding tests for its dependencies.

A continuous testing framework provides a wrapper around the set of tests so that they are applied consistently and prepare the way for automation. Developers want to be sure that the approach they take for a module is not dissimilar from those applied to related modules. When the modules evolve, so do a gamut of tests for interrelated software.

Frameworks provide a standard way to conveniently modify the scripts and functions for testing. Automation reaps gains when the inconsistencies in testing are removed, otherwise it generates a series of misleading test results.