When producing a tiny threaded fastener or manufacturing fuselages, building a small calculator app or releasing an extensive enterprise software, an attribute that is most common, unwavering, and paramount is quality. The job or activity which ensures that products, software, or services delivered are of the highest quality, is one of the most important activities in the entire life cycle of building a product or service.

In other words, testing and QA are critical and indispensable, However, the role and nature of testing have been ever-evolving and we already live in an era where the latest technologies are set to transform testingsoftware testing in particular. One of the key reasons for the emergence and prevalence of the stated technologies is that process efficiency and automation are no longer differentiating factors, but imperative for any organization. How can this transformation be achieved and what can be the chief ingredient infused to bring about this metamorphosis?

In the most simplistic terms, Artificial Intelligence is the acquired ability of a machine/program to follow human cognition. This means machines can become smart and learn to think and learn. This technology builds smart machines capable of performing tasks and taking decisions thattypically require human intelligence.

Theinception of the question of whether machines can think dates back to 1950, when Alan Turing and his Turing Test via Computing Machinery and Intelligence came into the spotlight. This flagged off the discussion, research and analysis on the topic of machines competing with humans in all intellectual fields. From then to now, huge progress has been made in the field of AI and the areas of its application have multiplied manifold.

Why do we require AI now more than ever in the field of Software Engineering? Some of the key factors which have propelled the research and development in AI for various facets of Software Engineering including development, testing and QA are as follows:

Organizations all over are under pressure and rushing towards replacing manual, rule-based, repetitive tasks with automation to transform into intelligent enterprises and gradually move towards autonomous enterprises. The repetitive tasks can be easily taken up by automation thus leaving humans free to be involved in more strategic, intelligent, and skilled tasks.

The inducement of human errors can beavoided if the routine unvaried tasks are performed by machines or programs which train and model themselves to attain flawless execution of the same.

It is the metamorphosis of organizations and businesses to intelligent enterprises by infusing digital technology in all areas of business, thereby transforming and revolutionizing the entire way end customer value is delivered. Digital Transformation has been a chief triggering point for the automation of processes and the use of AI in simplifying it.

Todays world is hyper-connected. This means that everything talks to everything and gazillions of information are shared over a network. With systems and machines communicating with one another, it opens new avenues for the usage of the acquired intelligence by machines and programs in automating and improving processes for the greater good.

Automation of tests and test cases are just tiny cogs in the wheel that transforms the software lifecycle and delivers quality products. Traditional record and play or other scripting tools do not require much intelligence. This is where AI pitches in because AI technologies involve some key pillars which act as differentiators.

With respect to Software Testing and Quality Assurance, the first aspect which AI can successfully address is the automation of the bulk of operational tasks. Some of the instruments in the bag are RPA, Chatbot mechanisms, Hyper automation et al. This will ensure that the QA and Test teams can focus on specialized, high-value tasks and reconfigure, strategic roles instead of repetitive activities.

The following are ways in which AI-driven smart automation aids Testing and QA:

While plain automation only performs a set of repetitive tasks, Smart automation uses bots and training models to improve and enhance the existing processes and reduces the probability of error apart from all the routine activities.

The work is fast, precise, and error-free as compared to the time consumed and issues induced in a manual scenario.

The plethora of automation tools used for different kinds of testing can be reworked and remodeled to include built-in intelligence by using cognitive models and algorithms. The result would be smart automation tools that are not simply Do as directed agents but continuous cognizant learners.

Thecrux of AI lies in analyzing enormous data sets, patterns, and relationships and deriving analytics on top of them to help in on the go decision making. How quickly and effectively it is done, determines whether the absorbed data set was simple or complex (thereby deciphering the Simplexity). When this salient feature of AI is used at the modular level or in end-to-end test scenario execution, the deliverable not only includes the desired output but insights and analytics as well.

Unit and API tests constitute the first and major chunk of the testing activities in the cycle. Generating these test cases or test suite through smart automation can act as a boon for the developers and testers alike. By studying and recognizing the step by step process, methods and coverage over a period, there can be an auto-generation of the desired tests, which will go miles in making the cycle quick and efficient and ease of the burden on the responsible employees.

Like the point above, process or test scenario documents can also be generated yielding identical benefits.

One of the key challenges in user interface testing is the change induced from time to time with every fix or the new development release. This wreaks havoc in test design as well as maintenance and execution. With AI-based smart models, there is improved recognition of complex and varied objects and elements along with intelligent analytical models to support a variety of frameworks. With the built-in mechanism for recognizing and capturing these new candidates, the paramount issue of UI testing is resolved to a great extent.

Modular and screen element testing constitutes a lower percentage and impact when we talk about the overall functionality and business associated. The complexity lies in ensuring that the system or application works as desired after integration with other systems, landscapes and includes intricate, compound, end to end scenarios. The dynamic adaptability and self-learning capabilities provided by AI ensure that this aspect of testing and Quality Assurance is handled well and, in a hands-free, error-free manner to top it all.

Applying AI to testing is not only about testing chunks of code or snippets of functionality or groups of integration scenarios. It can encompass and assist the verification and validation of gargantuan applications and software for huge businesses and enterprises having a strong foothold in a variety of industries and verticals like air crafts, shipping, textiles, food, etc.

Over the past few decades, we have seen several stages of evolving, regeneration and transformation resulting in the intelligent enterprises that all organizations are vying to become now. Enterprises in this cycle started from the Industrial Automation, moved towards Business Processes and their Automation got molded by the wave of Digital Transformation , geared up to become Smart Enterprises and are now moving towards the era of Autonomous Enterprises. At this juncture, it only becomes imperative that their Business Processes are automated intelligently, and the best practices are bundled smartly in self-sufficient packages to the extent of a plug and play perfection. This can only be possible through the application of Smart Automation using Artificial Intelligence.

From cars that drive themselves to the minuscule devices that can detect cancer cells to 3D Printers that work on their own and a neural network that can help spot Covid-19 in chest x-rays and many other ways, AI is transforming our world and everything in it.

In such circumstances, can testing and QA be untouched from its virtues? There is plenty of scope in this field with some use cases likebuilt-in intelligence in tools and IDEs, prepackaged content to be delivered, AI fortified robots and bots for quality checks of factories, units, websites, applications, and devices. The extensive usage of Natural Language Processing, Machine Learning, and Deep Learning to auto-generate smart, interactive, self-healing test suites is merely the prelude and thepath ahead is propitious.

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How Do AI and Smart Automation Impact Modern Testing and QA? - IoT For All