Test automation is among the many technological advancements that have been revolutionizing quality assurance. With every passing year, the test automation landscape has reinvented itself and restructured the quality assurance processes within organizations.
Being one of the best QA tools, enterprises have always made a conscious effort to adopt automation. However, since the pandemic, businesses have been automating their processes more aggressively, leading to continuous innovation in test automation.
Some of these next-gen test automation-related innovations have facilitated organizations to transform their software testing process and enhance customer experience.
Here are some of the cutting-edge innovations in test automation that have made a positive impact on quality engineering and customer experience:
Organizations need to adopt a shift-left approach if they want to survive and grow in this highly competitive market. In the past few years, automation has enabled organizations to move towards this transformation.
However, with technology advancements such as Robotic Process Automation (RPA) and generative artificial intelligence (AI), there has been a monumental evolution in the field of test automation. RPA enables software bots to replicate human actions, while generative AI uses AI algorithms and ML methods to perform tasks more effectively and rapidly.
Intelligent automation as a technology has significantly benefitted businesses across industries by enabling reduced testing cost, decreased time-to-market, better operational efficiency and improved security.
Leveraging autonomic systems in test automation is still in its early stages. However, the advantages of the autonomous systems have trumped the traditional software applications and tools used in test automation processes.
Autonomic systems are self-managed, self-adaptive platforms that constantly keep learning from an environment and accordingly makes independent decisions without any external intervention. These autonomous systems follow four basic principles:
These features of self-adaptive platforms make them very effective when crunching large data sets and employing complex analytical solutions. These autonomous data tools also help businesses in their respective customer-specific pain points by analyzing their customers’ big data infrastructure.
Moreover, the self-optimizing aspect of these autonomous systems uses cognitive intelligence driven by AI and ML, which makes them an effective technological option for innovative test automation strategies.
Codeless or low-code tools are prevalent among development teams globally. These tools help developers ease their burden by reducing repetitive and mundane tasks, while minimizing the risk of human error.
As the tools bring about a paradigm shift in the software development landscape, they are also making their way into the test automation arena. The main idea behind codeless or low-code test automation is that anyone can create automated tests irrespective of their coding experience.
Both development teams and QA teams are expected to deliver high-quality, quick and affordable service. Scriptless test automation can help organizations achieve this with minimum resources.
In most organizations, Site Reliability Engineering (SRE) has always been there in some form or the other. However, as companies are blending test automation into their development process, the need for having a dedicated SRE has increased.
SRE is quite similar to DevOps. One of SRE’s essential roles is knocking down the silos that exist between the development and operations teams, thereby ensuring seamless integration.
SRE also plays an important role in anticipating failures and automating those processes to take care of the failures before they appear.
In other words, SRE instills resiliency into the system through automation. Therefore, if your goal is to automate your complete process, SRE is indispensable for achieving it.
Both observability and telemetry are advanced forms of monitoring. These phenomena are also interconnected with each other. Without accurate data from telemetry, proper observability is not possible; without observability, you cannot make sense of the telemetry data.
Traditional monitoring can be done manually or automatically. However, if you want to complete automation for all your organizational processes, both observability and telemetry are imperative towards achieving it.
Businesses understand that failures are a part of the process. Observability as well as telemetry provide organizations with better visibility to understand the health of the systems, while automating the process to tackle potential errors by detecting them in advance.
Traditionally, most businesses have preferred mean time to failure (MTTF) as the core metrics for their IT systems. But it is no longer the case. System failures are inevitable. Sometimes it can be limited to just one component of the system, while other times, it can extend into the whole distributed system.
As organizations have realized this fact about system failures, they are moving towards achieving a lower mean time to recovery (MTTR). And chaos engineering plays a significant in achieving this (lower MTTR).
In chaos engineering, QA experts conduct experiments on distributed systems by testing them with real-world outage scenarios. It enables organizations to learn how systems react in unpredictable circumstances, such as byzantine failures, race conditions, a sudden large spike in traffic, etc.
Based on these reactions, businesses can formulate a strategy on whether, when and how to shut down the system if individual components in a system break down.
Chaos engineering is also an integral part of DevOps. Therefore, due to the DevOps frameworks’ automated nature, QA engineers need to analyze how organizations can mix chaos engineering with the organization’s test automation strategy.
Many industry experts have predicted the tremendous rise of test automation across the global industry landscape in the upcoming years. However, this expected boom in test automation also means that businesses must work on incremental delivery, measurement, and adaptation mechanisms. In addition, empowering quality engineering with test automation is an iterative process that relies on organizations’ ability to leverage the combination of advanced technologies such as AI, RPA, ML, etc.
However, not many organizations have the potential or the expertise to match this automation boom, which is why consulting with expert quality engineering service providers will be a good move. Qualitest’s tailored, AI-enabled test automation strategies and intelligent automation frameworks help businesses ensure precision, minimize risks and respond to escalating customer demands while preserving quality and ensuring superior customer experience.
