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Top 6 Testing Trends In 2021
The Automated Software Testing industry is growing each year at an exponential rate to cater to the ever-growing software testing needs in the market. No doubt the hype as Automation saves precious time and money for the organizations.
The year 2020 will be no different and we expect to see an even steeper rise in the demand graph for the new automation technologies on the board. Trending technology is the one who has proven its worth in terms of usability and profit. There are many young and growing technologies in the market which has the potential to become the trends for the year 2020, let’s take a look at some of these automation trends for this year:
IoT Automation Testing
According to Forbes, “the global IoT market can grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5 percent.”
IoT (Internet of Things) adoption is growing across different industries, across government sectors and in the daily lives of consumers. In addition, businesses are increasingly moving or evolving their IoT-enabled applications into the ...
... mobile app market, and rolling them out. The expected number of IoT devices is increasing exponentially every quarter and user demands in terms of technology, user interface and product quality are also rising rapidly.
Undoubtedly, IoT qualifies for the first and most selling automation testing for the year to come.
Robotic Process Automation
Robotic process automation (RPA) is the technological tools that partly or completely automate manual, rule-based, and repetitive human activities. They function by replicating the behavior of an actual human communicating with one or more software applications to perform tasks such as entering data, performing standard transactions or answering basic customer service queries. Yes, the “chatbot” that started to become ubiquitous on websites is almost always a robotic process automation resource, not a human. It can handle regular generic questions such as “where X is on the website,” “how do I reset my password” and the like.
As these technologies become more advanced they have begun to take on business process management tools features as well as artificial intelligence applications. It helps them to become even more effective and could lead, for example, to a point where the tool might evaluate the sentiment in a particular customer question or contact and make a discount recommendation.
It won’t be unsafe to assume that RPA is another coming age automation technique which is going to take over the automation industry in Software management business with a promise of profit and comfort.
Artificial Intelligence/Machine Learning in Testing
Artificial intelligence is one of the marketplace’s most overburdened buzzwords. “AI” conjures up images of things like all-powerful supercomputers, hell-bent on human destruction; voice-control assistant like Alexa or Siri; opponents of computer chess; or self-driving vehicles.
AI’s implementation in the software testing framework focuses on making the lifecycle of software development simpler. Through incorporating logic, problem-solving, and, in some situations, machine learning, AI can be used to help simplify and reduce the amount of repetitive and boring development and testing activities involved. But that is already done by test automation tools, right? Well, yes, they do!
What happens next, then? In this area we are in an active R&D, continuing to pursue more artificial intelligence and machine learning technologies to expand our software testing tool kit. There are many research avenues, but the end goal is clear: to help teams develop and test their code more efficiently and effectively, to speed up the development of higher quality software.
You may have encountered the term ‘DevOps’—a group of software development activities that incorporate development (Dev) and information technology (Ops) operations. DevOps’ purpose is to shorten the life cycle of development (SDLC), so teams can concentrate on developing apps, fixing bugs, and delivering frequent updates that match with business goals. In the same way, by incorporating software testing into the CI / CD pipeline, DevQAOps helps to increase the direct communication flow between testing engineers and developers, rather than making the QA team work independently. In short, DevQAOps is defined in two key principles:
Incorporating QA activities into the CI / CD pipeline
QA engineers will work in collaboration with developers and be involved throughout the CI / CD process.
A recent study indicates that improved customer satisfaction correlates to a low MTTR (Mean Time To Repair) in the production. Getting to low MTTR means an increase in all three of these categories: process of testing, performance and tooling. And that is where DevTestOps will play a major role. DevTestOps is thus expected to become one of the trends in test automation in 2020.
Blockchain technology has revolutionized the way businesses deal with digital currencies like Bitcoin. Such Blockchain implementations are not limited to the financial world, and its smart contracts are used from the energy sector to governmental services in every business field. Blockchain debugging brings new problems to this wide array of framework supports.
In fact, once the smart contract is enforced, its execution can not be reversed and thus, smart contract codes determine how the program works smoothly even with increased workloads. This whole Blockchain testing process calls for reliable outsourced next-gen testing services, specializing in debugging the code to produce successful Blockchain devices. Time to look for another trend in testing technology by 2020.
Big Data Testing
The name says it all: “Big Data.” The integration of a multitude of information handling systems has enabled the processing of truly massive amounts of data, particularly in the last decade. This information provides hugely valuable knowledge which requires new ideas and technologies to extract that value because of its very nature. Primary among these is the ability to create, store, retrieve and interpret these vast amounts of widely varying information quickly and accurately.
Big data tools are used as applications services which analyze and format the data they manipulate. By their very design, they involve indexing from the source data with many layers of abstraction to process results at reasonable timescales. Clearly, testing must look at all those same indexes and abstractions as well as check all the features of reductive analysis that make the raw data useful. The scalability of the system components must be checked across their operating range without processing a full load of content accompanying the operating scale.
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