Technology
Navigating the software testing market in the post-pandemic era
Global Banking and Finance Review
Company
The impact of the COVID-19 pandemic has been felt across multiple industries, and the software testing market is no different. Increased adoption of working-from-home practices and a shift towards the cloud have placed the software testing market under immense pressure and heralded the increased ado...
The impact of the COVID-19 pandemic has been felt across multiple industries, and the software testing market is no different. Increased adoption of working-from-home practices and a shift towards the cloud have placed the software testing market under immense pressure and heralded the increased adoption of AI-based automated software testing solutions to aid with remote IT maintenance and software applications.
Constrained resources were one of the most significant issues. Multiple employees, including DevOps, had no choice but to work from home without access to the same tools they would have had in the office. Moreover, with 11.6 million people furloughed in the UK, software companies had to adapt to the same levels of testing requirements, if not higher, with fewer DevOps available.
Working remotely has not always been the smoothest of rides either, with isolated DevOps team members unable to communicate as freely about quality concerns and processes in a timely manner. In addition, with the pressure mounting to release software quickly to allow customers to have a ‘mobile-first’ experience and keep businesses operating, the quality of software releases was put on the back burner, prioritising the speed of releases instead.
Finally, organisations had to rethink their delivery mechanisms by shifting their applications to the cloud and low-code/no-code platforms to allow borderless and seamless collaborations. As customers demanded newer features or reported new bugs, software updates and maintenance became challenging even with automated tests. This accelerated the need for AI-based automated software testing solutions to aid remote IT maintenance and software application development – a process that has become permanent in the current way of working.
However, simply accelerating application development is not enough due to the impact on quality. Slow-responding software with bugs leads to customer frustration and impacts retention. Organisations need to focus on Quality Assurance (QA) and performance testing, and finally replace legacy testing tools that are holding them back. Testing should be effective, efficient, and integrated with development to enable faster time to market at high quality.
When implementing robust software testing processes, DevOps need to be able to answer three critical questions – what to test, does it work, and does it scale? By using an AI-powered approach for end-to-end test automation, businesses can accelerate their testing by more than 10x, dramatically reduce business risks by 90%, and cut costs by 50%. Therefore, shifting from manual testing practices to a balanced, AI-powered quality assurance methodology is critical, with a few successful ingredients:
Self-service environments – setting up automated test environments with the help of software-defined Infrastructure-as-Code (IaC) and elastically scaled cloud capacity can dramatically reduce cycle times and costs.
Service virtualisation (SV) – a virtual environment helps capture, configure and simulate systems, negating the need to access the actual dependent systems and, as a result, creating more consistent test results through modelling the future scenario data.
AI collaborators – Machine Learning and AI can visually detect and identify user interactions, creating desired application logic. Once teams identify what AI should be looking for, the cognitive engine enables automation to keep track of changes, provide instant feedback, and minimise maintenance of the automation.
Automated impact analysis – evaluating the impact of changes made allows organisations to prioritise their efforts based on value and risks. As a result, less relevant tests can be parked or left off the test run entirely, thus reducing the effort and cost of testing.
Whilst automation of tests and deployments is beneficial for the software’s health, in some cases organizations may also need some manual User Acceptance Testing (UAT) and human verification. Automated testing should be continuous; however, it must be applied strategically without undermining business agility.
There are a few things organisations need to keep in mind when applying test automation:
With the pandemic slowly becoming a distant memory for many, the effects of the seismic shift it created in how organisations work and operate are still very pertinent. According to Gartner, almost two-thirds of application software spending will be on cloud technologies by 2025, and those who do not adapt risk being left behind in the low-growth market.
As organisations continue shifting to the cloud and accelerating their digital transformation strategies, they must maintain focus on improving their software performance. Continuous and strategic automation that uses intelligence to address critical challenges can help free up DevOps’ time and allow them to change their focus to creative solutions and critical thinking, opening up channels for real innovation.
AI adoption might still be raising multiple questions, however, harnessing the full power of the available AI and ML tools will empower teams within the enterprise and further connect developers and testers. This will result in more efficient projects contributing directly to the business value and enhancing efficiency and speed of product launches
DevOps is a set of practices that combines software development (Dev) and IT operations (Ops) to shorten the systems development life cycle and provide continuous delivery with high software quality.
Automation in software testing refers to the use of specialized tools to execute tests on software applications automatically, improving efficiency and accuracy while reducing manual effort.
AI in software testing involves using artificial intelligence technologies to enhance testing processes, enabling smarter test automation, predictive analytics, and improved decision-making.
Cloud technologies refer to the delivery of computing services over the internet, allowing for scalable resources, storage, and applications without the need for local infrastructure.
Quality Assurance (QA) is a systematic process to ensure that software products meet specified requirements and are free of defects, focusing on improving and maintaining quality throughout the development process.
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