The majority of modern software development companies dream about frequent deployments. The truth is that, though, many are far from reducing their development cycles. How come? testing remains the biggest bottleneck of a software development lifecycle. In an ideal world with successful continuous delivery and quality built into the software, teams need to be committed to running many different tests, both automated and manual, continually throughout the delivery process. That being said, achieving continuous testing isn’t easy if challenges remain unsolved. Today, let’s talk about the main challenges in continuous testing and how to cope with all of them.
It All Starts with Test Design
Be prepared: you might face challenges in continuous testing as soon as in its early stages. Things get tricky in the test design phase when your team writes down the requirements. The problem is, requirements definition is still a manual process, which means it’s time-consuming and not always accurate. To make matters worse, requirements can change within a single sprint. The results of the manual requirement definition are usually gaps in test coverage or too many test cases that overlap with each other. These mistakes will cost you a lot since they affect all further testing. In fact, poorly defined or incomplete requirements are responsible for 64% of the total defect cost.
What can we do about it? The solution is automation. Automating test design will help you reduce testing time and eliminate defects. You can use a test design automation tool that will help you generate the exact number of test cases needed and provide sufficient coverage. Also, to manage requirements properly, try using modeling, i.e., create a visual model of the system under test. A flow chart will be more accurate than traditionally documented requirements and will later become the primary source for test case creation.
Test Data Management Will Cause You Trouble
Another challenge in continuous delivery is locating proper test data. According to Continuous Testing Report 2019, 55% of respondents spend 30-60% of their total testing time on test data management activities. This means that test data management (TDM) done right can increase your team’s overall efficiency. To reduce the time spent on TDM, you have to take care of test data provisioning.
The main difficulties associated with it include:
- extraction of test data spread across multiple databases
- limited access to production systems
- maintenance of multiple dataset versions for different tests
- creating test data without copying the production data
To deal with all these, you need to revise your test data management practices. Obviously, you need to apply automation in test data generation and use reliable TDM tools. Also, to comply with new data privacy regulations, consider synthetic data creation.
Test Environment Management Won’t Be Easy
Within continuous delivery, tests run in parallel in the development phase and integration phase. Because of that, your team needs to have many environments available for different purposes. Sadly, the lack of test environments is one of the biggest challenges to achieve continuous testing. What’s more, even if you have multiple environments, your team may find it difficult to coordinate all of them as building, maintaining and managing test environments is expensive and time-consuming.
That is why modern and agile companies choose private device cloud solutions. Cloud-based environments are accessible anytime and easy to manage. Moreover, virtual environments don’t require complex physical infrastructure and back-end systems. Today, virtual environments are necessary for successful Agile and DevOps adoption. Using cloud-based testing environments cuts down the wait time significantly and assures the product quality in a timely fashion.
Lack of Proper Tooling to Automate at Scale
In an ideal world, agile teams that strive to build quality into their software at all times should be able to continually execute as many tests as possible throughout the delivery process. Yet, a majority of teams are struggling with low testing throughput and a plethora of unexecuted tests in the pipeline. So what is the problem? The rigid test automation tools that they use are to blame.
Traditional automation tools are built on legacy technologies and have the following key issues that are attributed to the failure of reaching continuous testing.
- Limits on concurrent test sessions
- Slow test execution
- Hard to automate tests at scale
These issues are often overlooked especially when software teams just start to build their automation framework. But they will soon surface as the number of tests and the need for automation thrives.
The solution to that is unlocking the concurrency limits to increase throughput and improve testing efficiency. Whenever a compatibility test suite is under execution against a big number of real devices or browsers, for instance, developers would still be able to perform CI tests, unit tests, etc. on one or two spare devices in parallel without losing time to wait for the compatibility test to complete. At the end of the day, any slowness in test execution or long queueing kills continuous testing.
Is Test Orchestration Even Possible?
Don’t you just hate it when test automation processes seem fragmented and nothing is coordinated? To create a working software delivery pipeline, you have to put different pieces together and make sure they work properly. But this seems like an incredibly challenging task for testing alone.
Luckily, it doesn’t have to be. Successful test orchestration and execution are possible when your team is prepared for their job. The truth is, continuous testing requires changes in workflows and processes but most importantly, in the mindsets of your workers. To test continuously, you have to hold training programs that will help your testers acquire new skills like writing automation scripts. Your software developers, on the other hand, will have to learn more about test-driven development. Basically, you should form interdisciplinary teams that can collaborate closely and maintain the entire continuous delivery process.
Beyond that, the difficulties in test orchestration can be solved with AI. Implementing machine learning and artificial intelligence will help you with the “smart” orchestration of testing. AI-powered tools can determine which tests are required at particular development phases without manual intervention. Take AI Testbot as an example. It helps you with device compatibility, performance and exploratory testing, and does all that without you having to bother about designing test scenarios or coding.
Wrapping Up
Continuous testing isn’t easy, and there are challenges most of the companies may face. To realize its full potential, you first need to automate everywhere possible to move fast, like test design, test orchestration, test execution, etc. Although test environment management could also become a big headache to any agile team as it requires dedicated resources, adopting a private device cloud with unlimited concurrent testing capability would help to make continuous testing a reality.
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