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For 15 years now, we’ve published our annual World Quality Report with the intention to help companies look at quality and testing across different industries and lines of business, learning from other companies how to do things better, quicker, and more efficiently.
As you’d expect over this period, a number of trends have emerged – some being predictable, and others less so. Some considerable regional differences also emerged, which you will find enlightening – and even surprising, dependent upon where you are based.
Perhaps the biggest single consistent trend across the 15 years though has been the drive towards automation. And this won’t surprise anyone as it’s human nature to pursue ways of simplifying work, and automation is probably one of the best fruits of this pursuit.
But with today’s accelerated pace of delivery demanding lightning-fast development speeds and adherence to quality benchmarks, QE Automation is often seen as the beacon to deliver seamless results. However, when we look into the detail of the report’s findings, two factors emerged as being the main blockers to automation.
Legacy and skill shortages continue to be the major blockers
The first of these was the impact legacy systems was continuing to have, with 34% of respondents either experiencing problems replacing their legacy systems, or the associated impact their legacy decisions were still having. In some recent cases that we have worked on, organisations are having to deal with systems that were implemented 20, or even 30 years ago.
And in almost every case the people who made the decisions are long gone – and so is the context behind a lot of the decisions they made regarding that particular system or application. What looks like a simple case of taking an old – but possibly key – on-premise system and migrating it into the cloud soon starts to unearth many difficulties including (but not limited to):
- lack of documentation or source code
- patch upon patch of changes and workarounds often built around business logic that is no longer relevant
- technology that reached its end-of-life years ago and the vendor no longer exists
- modern tooling not supporting or incompatible with legacy systems.
Lack of available skills (30%) was the other significant blocker to automation adoption, so clearly there is a continued need for developing technical skills to deal with both existing and upcoming technologies. Despite these two significant blockers though, lack of funding does not appear to be an issue, which is encouraging as organizations search for potential solutions to tackle these blockers.
So, is AI the answer? Or just another question?
It will surprise no one to see the appetite for AI to be used in automation and testing activities. More than 50% of the respondents were eager to see testing AI with automation, which is well ahead of the other top focus areas like security and mobile.
It’s clear that the potential of Generative AI systems is immense. These powerful systems have the potential to transform all kinds of areas, including software quality engineering activities. However, the survey also shows that organizations need to apply Generative AI step-by-step, with enough expertise to ensure sufficient reliability, effectiveness, and efficiency of the outcomes. While delivering good systems with quality is a priority, it also must be done in a timely manner. Quality engineering & testing teams must adapt to working with AI and testing AI systems cautiously.
And finally, despite the promises of low code/no code and automation-based frameworks, only 19% of respondents are currently using such tools, with nearly 70% of respondents at the stage of testing a pilot. While low-code type tools can allow an easier entry point into automation, there is still a long way to go before they truly challenge more traditional and feature-rich automation options, especially in terms of productivity and efficiency.
In summary…
This is just a snapshot of the report’s findings, so we’d encourage you to request a copy of the report to explore in more detail. What is clear is that legacy systems and their associated decisions, along with a shortage of available skills continue to have a major impact on automation teams everywhere.
So, it is important to capitalise on your current position, while investing in the future. To do this, equip teams with the right skills to understand these legacy systems and technologies, while also adapting and adopting new technologies like AI systems, which have the potential to genuinely transform future operations and processes.
Our research leads us to the following recommendations to increase the value from your quality automation initiatives:
- Continue to expand on what works – don’t adopt new automation technologies or platforms for the sake of it or just because they are new or trendy. There is elegance in simplicity; continue using the frameworks and solutions that are adding value. When new solutions arise, thoroughly evaluate them first before pushing for full adoption.
- Automation can be prioritized against legacy systems – focus on automating the test scripts and technologies which yield a larger and faster return on investment first. Once you have achieved the desired coverage, capability and speed to market goals, only then move on to the more challenging technologies.
- Upskill teams to use AI and to test AI systems – AI-based systems are here to stay, whether used for testing or the focus of testing. Develop training plans and career pathways so that your organization knows how to leverage and test these systems.
- Establish an Automation Marketplace platform – enables an “automation as an asset” approach, empowering development, infra and business communities to leverage automation test scripts which have been created. Your automation engineers own the development and maintenance of the scripts while other teams integrate them into their daily routine.
