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How AI and Smart Automation is used in Modern world

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Use of AI and Automation for solving Modern problems

When producing a small threaded fastener or manufacturing fuselages, building a little calculator app or releasing an in-depth enterprise software, an attribute that’s commonest, unwavering, and paramount is quality. the work or activity which ensures that products, software, or services delivered are of the very best quality, is one of the foremost important activities within the entire life cycle of building a product or service.

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In other words, testing and QA are critical and indispensable, However, the role and nature of testing are ever-evolving and that we already sleep in an era where the newest technologies are set to rework testing–software testing especially. one of the key reasons for the emergence and prevalence of the stated technologies is that process efficiency and automation are not any longer differentiating factors, but imperative for any organization. How can this transformation be achieved and what are often the chief ingredient infused to cause this metamorphosis?

What Are the Capabilities of AI?

In the most simplistic terms, AI is the acquired ability of a machine/program to follow human cognition. this suggests machines can become smart and learn to “think and learn“. This technology builds smart machines capable of performing tasks and taking decisions that typically require human intelligence.

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

The Need for AI in Software Testing and QA

Why can we require AI now quite ever within the field of Software Engineering? a number 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:
Handling Repetitive Tasks

Organizations everywhere are struggling and rushing towards replacing manual, rule-based, repetitive tasks with automation to rework into intelligent enterprises and gradually move towards autonomous enterprises. The repetitive tasks are often easily haunted by automation thus leaving humans liberal to be involved in additional strategic, intelligent, and skilled tasks.
Minimizing and Eventually Eliminating Human Errors

The inducement of human errors is often avoided if the routine unvaried tasks are performed by machines or programs which train and model themselves to achieve flawless execution of an equivalent.

Digital Transformation

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

Hyperconnectivity–A Connected Virtual World

Today’s world is hyper-connected. this suggests that everything talks to everything and gazillions of data are shared over a network. With systems and machines communicating with each other, it opens new avenues for the usage of the acquired intelligence by machines and programs in automating and improving processes for the greater good.
The Impact of AI and Intelligent Automation

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

With reference to Software Testing and Quality Assurance, the primary aspect which AI can successfully address is that the automation of the majority of operational tasks. a number of the instruments within the bag are RPA, Chatbot mechanisms, Hyper automation et al. this may make sure that the QA and Test teams can specialize in specialized, high-value tasks and reconfigure, strategic roles rather than repetitive activities.

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

Enhancement rather than Repetition

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

Speed and Accuracy

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

Embedding Intelligence in Automation Tools

The plethora of automation tools used for various sorts of testing are often reworked and remodeled to incorporate built-in intelligence by using cognitive models and algorithms. The result would be smart automation tools that aren’t simply “ Do as directed “ agents but continuous cognizant learners.

Utilizing the Essence of AI and Decoding ‘Simplexity’

The crux of AI lies in analyzing enormous data sets, patterns, and relationships and deriving analytics on top of them to assist in “on the go “ deciding. How quickly and effectively it’s done, determines whether the absorbed data set was simple or complex (thereby deciphering the ‘Simplexity’). When this salient feature of AI is employed at the modular level or in end-to-end test scenario execution, the deliverable not only includes the specified output but insights and analytics also.

Automated Test Generation

Unit and API tests constitute the primary and major chunk of the testing activities within 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 are often an auto-generation of the specified tests, which can go miles in making the cycle quick and efficient and simple the burden on the responsible employees.

Document Generation

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

Smart Models for interface Testing

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

Integration and End to finish Scenarios

Modular and screen element testing constitutes a lower percentage and impact once we mention the general 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 finish scenarios. The dynamic adaptability and self-learning capabilities provided by AI make sure that this aspect of testing and Quality Assurance is handled well and, during a hands-free, error-free manner to top it all.

Not Limited to Testing of Code

Applying AI to testing isn’t 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 robust foothold during a sort of industries and verticals like air crafts, shipping, textiles, food, etc.

Evolving of Intelligent Enterprise: Business Processes and Best Practices

Over the past few decades, we’ve seen several stages of evolving, regeneration, and transformation leading to the intelligent enterprises that each one organization are vying to become now. Enterprises during this cycle started from the economic Automation, moved towards Business Processes and their Automation got molded by the wave of Digital Transformation, ready to become Smart Enterprises and are now moving towards the age of Autonomous Enterprises. At this juncture, it only becomes imperative that their Business Processes are automated intelligently, and therefore the best practices are bundled smartly in self-sufficient packages to the extent of a “plug and play” perfection. this will only be possible through the appliance of Smart Automation using AI.

The Path Ahead

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

In such circumstances, can testing and QA be untouched from its virtues? there are many scopes during this field with some use cases like built-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 tongue Processing, Machine Learning, and Deep Learning to auto-generate smart, interactive, self-healing test suites is simply the prelude, and therefore the path ahead is propitious.

source: iotforall

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