Top 5 Challenges in Automation Testing and Analytics (and How to Overcome Them)
Continuous testing remains a labor-intensive task for most DevOps teams. A lot of manual testing still happens. Huge gaps exist when it comes to implementing test automation in a reliable, stable, and efficient way throughout the DevOps pipeline.
As a result, DevOps maturity is held back instead of being supported.
Test automation and analysis have become top priorities for digital enterprises as part of their DevOps journey. However, there are challenges and roadblocks with achieving those goals.
Let’s explore five specific challenges of test automation in more detail, along with how teams can work to resolve them.
Top 5 Challenges in Automation Testing and Analytics
1. The Lack of Necessary Skills
Test automation is an art. But, not everyone is an artist. Although test automation offers teams a number of benefits, not everyone has the right skills to do it successfully.
Because of this, manual testing still occurs as teams compensate for any existing automation gaps. Or, they see it as a stable alternative.
This results in delays and a disconnect with the R&D team, who is usually two or three steps ahead. In order to match their pace, testers need to complete their cycles in four to six hours.
But manual testing cannot meet the development pace. It is then pushed to the end of the cycle. As a result, teams need to add a manual regression — also called “stabilization phase” — at the end of the sprint. Instead of reducing the release cadence, they extend it.
2. Teams Don’t Stabilize Tests Before Automating
For teams who are just getting started with automation, the process is similar to riding a bike. Before you take off, you need balance.
We often see DevOps teams building all their tests and running them together without stabilizing first. The result is failure, frustration, and trust issues with automation. Because of this, teams revert back to manual testing.
The best practice for building a test automation foundation is this modular execution approach:
- Build your test locally over a real device/browser.
- Run your test daily to ensure a balanced execution and that there are no blockers.
- Tie your execution to CI and run your test constantly (at least nightly).
- Increase your digital platform coverage and execute more frequently.
- Maximize your automation coverage up to 90% (to get into the DevOps-friendly zone)
3. Lab Management is Burdensome
Today, there are still teams that prefer to build and maintain their own labs. This isn’t necessarily a bad thing. But, in-house labs are hard to manage — and expensive.
With new operating systems, devices, and browser versions consistently being released, labs can quickly become out of date. As a result, teams can spend a lot of time maintaining and running their lab as opposed to testing.
An executive at a large US-based bank recently made this point about the issue:
“We are in the business of creating and maintaining the bank’s testing activity. We are not in the business of managing IT resources. This tradeoff should always guide us, and we should keep the balance in favor of testing at all time.”
Having a cloud-based lab is key for continuous testing unless there are some special testing requirements/scenarios with IoT, special networking (especially in the Telco space), etc.
4. Test Automation Becomes Dynamic
Different from the Waterfall methodology, the DevOps process requires unique testing capacity at each and every step of the software development lifecycle (SDLC). With that said, the execution layer should be geared towards automation at scale. This allows teams to scale up or down at different points of time.
In order to achieve coverage metrics, teams should be able to run their tests in a parallel mode and define priorities for executions. They should also be able to queue and better orchestrate their overall testing process between teams.
The best and most successful way to approach automation is to start small and then scale. However, many teams do not have the right tools to scale appropriately. When teams don’t have the right tools, it creates a disconnect with the R&D team. Automating at scale requires the right combination of people, skills, and tools.
5. Sorting Through All the Data
When DevOps automate at scale, one of the results is a huge influx of data. That data needs to be reviewed and analyzed. Teams often find they are swimming in a sea of data made up of log files, architects, and test results. However, this data does contain a lot of useful information. The challenge is actually spotting it.
For example, insights from data can inform R&D teams on what fixes need to be made. Making sense of all the data is a big undertaking for many teams, especially those that are not equipped with the right tools.
Effective triaging and sorting through the noise is key for achieving fast feedback. Today, the reality of CI/CD is that it requires teams to execute in minutes and analyze in minutes, understanding where problem is.
Challenges in Automation Testing Can Be Eliminated With the Right Tools
If DevOps teams want to be successful with continuous testing, they need to leverage modern tools. When evaluating different solutions, there are four components teams should look for that are critical to success:
This refers to test automation creation and the tools used to do that. Whether a tester is an automation ninja or a manual user with basic knowledge of the business flow, the tools should be agonistic to the level of user. Ultimately these tools will allow manual testers to begin executing automation while advanced testers can focus on higher priority tests.
This refers to the ability to successfully scale test automation operations. The solution should offer capabilities that help teams take a small, locally-run test and expand it across multiple platforms, devices, and browsers. This ensures the right scale and coverage.
In today’s competitive world, teams need to have the ability to conduct a test at anytime. A comprehensive solution to this need would be to provide open access to the lab and equips teams with the right tools to run and perform tests. This ultimately helps them be adaptable and keep pace with the new releases.
To achieve success with continuous testing and automation, teams need to be able to effectively see clear and accurate test results quickly. What’s more, they need to identify problems quickly. Remember, automation creates noise and false negatives, so teams need to be able sort through this to provide the necessary evidence.
The only way to successfully address the top challenges in automation testing is to use a solution that combines the four components listed above.
If a solution is missing one of the components, a critical part of the continuous testing process is lost. What’s more, there is a good chance teams will spend a significant amount of money accounting for gaps or compromising quality.
As teams make their way towards effective continuous testing, achieving good test automation and analysis is key to that success.
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