Apathy killing off interest in govt datacentres

Mistrust, lack
of resources 

Apathy hovers over
govt datacentres.

Friday, Feb 28th

AI, ML up to the task and the test

AI, ML up to the task and the test Artificial intelligence (AI) and machine learning (ML) are technologies that have long since left the moniker of 'emergent' behind. These tools have become so embedded into systems and solutions that they are reshaping the organisation's approach to development, automation, and, of course, testing.

AI and ML offer immense potential to enhance testing capabilities and to bridge the gap between human and machine.

As AI and ML have evolved within the technology domain, so have they evolved within the testing domain. From AI-powered testing tools to systems that can test themselves as they learn and self-heal, the applications have become incredibly powerful and ubiquitous.

At the moment, AI and ML can be used to expand on the capabilities of differential testing, visual testing, and declarative testing. They have also shown immense growth in their ability to self-heal within automated systems.

AI, ML up to the task and the test

For the Quality Assurance (QA) engineer, AI and ML have allowed for far better approaches to problem-solving and far faster results. Testing has always been a case of try, try and try again, often demanding that teams spend hours retesting new code and new changes that take time to complete and troubleshoot.

While this is the name of the testing game, there's no reason why testing can't get the benefits of speed, automation and intelligence analysis offered by AI and ML.

AI and ML have introduced a new layer to the testing space, a layer that allows for deeper integration, more agility, faster release cycles, and more robust tools and frameworks.

Using these tools and technologies, DevOps undergoes a shift from humdrum and repetitive to scale and smart testing and collaborative development. AI and ML are the intelligent support staff that never sleep and always do as they're told.

Another advantage of ML and AI within testing is the price tag. Traditional testing takes weeks and the price tag often grows alongside the hours it takes to undertake rigorous testing.

Manual testing has always been time-consuming and expensive because it requires absolute attention to detail to get the right results. With AI, the intelligence of testing is given a boost, speeding up the amount of time required to painstakingly assess and test.

If used correctly – and this is the most important point – then AI can save time for developers and significantly reduce the length of time spent undertaking robust testing. This will, in turn, cut costs while allowing for more time to refine development or invest in new solutions. By automating the testing process, you're reducing costs, improving testing speeds, and improving quality.

Alongside speed, quality, improved maintenance times, evolving intelligence and more room to wriggle when it comes to ongoing development, AI and ML are critical to ensure that QA can evolve to meet market demand.

The 2018-2019 World Quality Report found that the role of QA and testing has changed. It has become the 'enabler of customer satisfaction and business outcomes', a change that has impacted on the role, the requirements and the outcomes of testing and QA.

The same report pointed out that AI can potentially empower the enterprise by giving it the ability to create self-generating, self-running and self-adapting testing. Already, around 57% of respondents had implemented testing projects with AI and ML with 55% still ascertaining the best ways in which to use AI to drive testing and QA.

For the enterprise, AI and ML open the door to enhancing testing and development capabilities in such a way as to drive customer engagement and business growth.

These technologies can accelerate the speed at which solutions are developed and tested, allowing for improved time to market and the opportunity to explore new products and solutions at a far lower cost than in the past. AI and ML can also potentially catch the bugs in the code that the testers miss, further reducing the risk of error and customer frustration.

These benefits may sound delightful but, of course, there are always caveats. AI and ML are not going to step into any business and utterly transform the testing landscape if they're not implemented correctly or if the wrong solution is wedged into the right hole. AI and ML testing frameworks need to be integrated into the business in such a way as to ensure that they enhance existing systems and teams.

Markets are competitive, the economy is flat and the demand for customer attention is beyond fierce.

With AI and ML frameworks embedded into your organisation you can ensure that you can meet these challenges with increased agility and capability. Just find the right partner and toolkit to make sure that these technologies are embedded and used correctly - it's not plugging a hole; it's transforming the business whole.

By Mandla Mbonambi, CEO of Africonology.

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