It’s safe to assume that by now, everybody is well aware of AI and its potential adverse effects on humanity and it’s been on everyone’s mind for quite a while. We thought it would be a great idea to explore more pragmatic and short-term implications of AI, like how it can improve some facets of our professional lives. Namely software testing. 

There is now a body of research published throughout the last few years that AI is soon to become the “hottest new thing” in software testing. It is projected to improve the work efficiency of QA engineers all over the world and help them overcome the standard issues commonly associated with their field.

In this article, we want to explore the ways AI can improve software testing and why you should stay tuned for the innovations in this emerging niche. Let’s dive right in, shall we? 

Non-deterministic testing

While philosophers are still debating whether humans possess free will or are purely deterministic beings, it’s essential to underline that there’s nothing deterministic about the algorithms that govern the decision-making of AI. This is a crucial complement to software testing. 

Most probably the best document published to date on the non-deterministic character of AI-assisted software testing is the “Test Automation for Machine Learning: An Experience Report,” posted by Angie Jones, a senior software engineer at Twitter. 

A non-deterministic approach to software testing has proven to be much more thorough, compared to what a human could have executed, due to the limitations related to the nature of human thought. However, it’s essential to stress that there are also specialists that are against non-deterministic approaches in testing as well. 

Increase efficiency and client satisfaction

AI has the potential to considerably impact the amount of time developers will have to spend on tasks like writing scripts and analyzing massive datasets. AI can replace developers on tasks like sorting through logs, thus allowing them to make a broad spectrum of processes less prone to error and executing these tasks much faster. 

Obviously, various test methods have their own shortcomings. When it comes to manual testing, even the most sophisticated software demands very straightforward and even simplistic approaches to testing like, clicking individual buttons in a particular order, ticking certain boxes, and so forth. While this type of testing is undoubtedly essential, it’s also known for being very time-consuming. 

Thorough manual tests are very time-demanding. Writing scenarios for these tests unnecessarily capitalizes on the developers’ time. 

AI allows tackling this issue on both ends by eliminating unnecessary distraction on the developer’s end and skyrocketing the quality of the manual test. AI-powered tools can thoroughly analyze the log files, which will allow to considerably increase the correctness of the manual tests.

Predicting bugs before they arise

The MIT Technology Review has briefly covered Ubisoft’s AI tool that is designed to spot code errors, allowing developers to detect issues at the earliest stages of game development. As you may have anticipated, they’re doing this in order to minimize the costs associated with bug fixing.

Identifying bugs is a demanding task, and Ubisoft reported that it could often consume 70 percent of the budget for a game that they’re working on. 

The AI is trained to identify certain lines of code that were previously associated with bugs in previous projects and immediately flags the problematic parts of the script.

This type of tools is expected to become much more widespread, allowing to minimize human error before it can have an adverse negative effect. 

Predicting your customers’ requirements

There is now astonishing demand in the tech industry, which underlines the importance of exceeding your clients’ expectations, in order to stand out from a large pool of competitors. 

We asked Jeremy McCoy, the head of marketing at IsAccurate and Grab My Essay how artificial intelligence can improve a business’s approach towards their customers’ requirements. Here’s what he had to say: “AI can have an impressive contribution in providing your customers with impeccable services, along with being able to use its predictive capabilities to understand what drives your clients, what their next steps are, and more importantly, understand what they actually need. This will allow you to be a few steps ahead and build a strong partnership with your clientele.”

Making testing less expensive

The later bugs are identified, the greater the financial toll they’re going to have on the development process. As we mentioned previously, the predictive capabilities of AI allow teams to identify bugs at the earliest stages of development and massively reduce the costs of these errors.

A study published in the Journal of Information Systems, Technology, and Planning, called “Integrating Software Assurance Into the Software Development Life Cycle,” reports that dealing with a single error after the product’s release can be as high as four times more costly than in the design phase. The same study indicates that it can be a hundred times more expensive at the maintenance phase. Here’s a figure published in the above-mentioned paper: 

AI will enhance our roles

AI will also have an impact on the “shape” of the work we do. At this point, we can only speculate how exactly the QA roles associated with AI will be named, and the spectrum of their responsibilities. However, some companies have already started thinking about how AI will impact our job descriptions. 

For example, the World Quality Report that we mentioned above considers that it is most likely that we’ll be seeing more of the following new roles:

  • AI QA strategists — their responsibilities will be rooted in understanding how Artificial Intelligence can be applied to various businesses, and how that can facilitate and enhance software testing.
  • Data scientists — while this is by no means a new role in IT, these specialists will have to analyze test data and make use of predictive analytics and statistics to build models.
  • AI test experts — these professionals will be responsible for testing AI applications. Besides having an in-depth understanding of QA principles, their responsibilities will also have to do with ML algorithms and NLP techniques.

The reason we’re still not entirely sure about the way these roles will crystalize over the years since these phenomena very much depend on many external factors. Maria O’Neil, an HR manager at Studicus and WoWGrade, told us that many conventional IT roles today have evolved to their current form over time, and will continue to shift shapes. Like UX designers, for instance. While this role was an inexistent 15 years ago, it’s slowly starting to morph into other, newer ones today, such as Product Developer, and others.


There is no doubt that we’re now living in a perpetually evolving world and our job descriptions mimic the technological progress we’ve embarked on. Artificial Intelligence is certainly a central factor when it comes to changing the way QA engineers will be working in the years to come. A new era, where the efforts of Quality Assurance engineers are intertwined with AI, will most certainly bring us more efficient and accurate software testing. 

It’s time to buckle up. 

Dorian Martin is a frequent blogger and an article contributor to a number of websites related to digital marketing, AI/ML, blockchain, data science and all things digital. He is a senior writer at Supreme Dissertations, runs a personal blog NotBusinessAsUsusal and provides training to other content writers.

Drones With Revolutionise Various Industries

Drones With Revolutionise Various Industries

Are you not curious of using drones while flying for capturing of videos from different angles ? Drones are yet very old technology and and majority of defence industry was using it from time of world war 2 . At later stage , its potential has  been explored by business industry which have started to immerse in human life more .

Over the time drones technology has continuously been improves and have been powered with numerous of functionalities . Let’s look at the one of the trending drone named as ” Autonomous Drones” .

Let’s quickly understand about this .

Autonomous Drones 

It has been seen as general trend that wherever  drone is seen , there is a person behind it who is operating it with remote control . Those primitive devices are now being replaced with new autonomous one . These devices can even be operated without pilots . The location of these drones can be monitored remotely by pilots and pilots can take control of them when it is absolutely necessary .
These technology giant autonomous device has capability to fly under navigation provided by GPS by using GPS ‘waypoints’ that facilitates autonomous fly possible .
Another category of drones has drones with flying capability even without availability of GPS . These devices are freed from various limitations and will fly autonomously in pilot’s sight or beyond , or may have got some limitation .
In present scenarios , various industries are making use of these Drones to fulfil their business goals . Drones are not being used to accomplish various task such as food delivery , asset inspections , inspection of dangerous areas and much more . Drones are more actively being used in industries like construction , transportation , agriculture , and more .

Drones With AI And IOT

Since drones are acquiring majority in marketplace  , technology experts are trying to make it more technically giant with use of AI and IOT .
Drones which are inbuilt with sensor are being used in agriculture industry to monitor crops and plants so that bugs data in them can be acquired with ease .
Making drones AI enabled with make the drone smarter . Imagine Drone is recommending minerals that should be given to plants while it is getting infected . These functionalities are gaining popularity in various parts of the world . If AI and IOT has been integrated successfully in Drones , it has potential to completely change revolution making less human intervention .
Now that the positive side of Autonomous Drones . Let’s look at some challenges that autonomous drones might face in practical situation :

GPS Accuracy 

Although the newest category of autonomous drones are inbuilt with camera and deep learning , they are capable enough for autonomous navigation , but AI fails to offer expected outcomes . As a result pilot fails to get access of data for manual control .
There is still huge requirement of transmissions with higher bandwidth for remote monitoring . All we can do it just wait and watch for getting this problem solved and get technology advanced .

Connected Drones 

With the help of IoT Technology integration , connected drones will gain its significance . Autonomous drones which is connected with cloud will provide various advantages right from flying drones with remote operations , integration of new AI models in drones , updating of drones firmware  directly from cloud , real time data recording and more .
These functionality with benefit various industries if pitfalls arising out of it will be solved .

Industrial Utilization Of Drones 

Autonomous drones can be implemented across various industries which can provide us with real time insights to certain aspects , carry on weights which can be used by retail stores to arrange product delivery reducing human labour .
However the biggest challenge would be integration of technology and deployment of algorithms to meet customised task of industry .
But how these all will get possible totally depends on the outcome of future . Till the time AI & IOT technology experts explore more ways to make autonomous drones more technology advance . Our developers at Winklix is fully compatible to explore drones feasibility . Autonomous drones has lot more capabilities which are yet to be explored . 

What Is Machine Learning & Its Applications

What Is Machine Learning & Its Applications

In past few years , we had been experienced many companies who finds ways to incorporate artificial intelligence (AI) in they systems . As being one of the emerging technology , AI exploration has bought about sub concepts . AI as a technology which concept is machine learning in which  computers has ability to learn without being programmed for it . 

Machine Learning 

As this article is about Machine Learning (ML) , the question that arises in everyone mind is what exactly is machine learning (ML) ?  In simple terms ML is known to be subset of AI wherein computer algorithms are being used which has ability to learn automatically from data and information , even without being programmed for it .
So computers learn data and information on the basis of algorithms which aids them to build predictive models on the basis of observation . ML is being used for prediction and not perfection which are good enough to be useful .
Nothing in the tech world is simple and the same exist with machine learning . ML concept totally depends on how much data is being provided using supervised learning , unsupervised learning and other types of learning . Their type of learning is based on the information it’s fed labeled or not .

But the question arises how will an ML enabled device undergoes training ? The amount of training initially depends on how much data system is providing initially . ML concept can’t do anything without presence of data , but how much data is to be initially provided to the system ? Before going any further about why to use ML and other stuff , lets get into details about what exactly they are .

Supervised Learning 

Supervised learning is one of the process of ML wherein we can say system is provided with data (x) by defined algorithms and in respective of data x output (y) is also labeled . Due to correct labelling of input and output data , system gains capability to recognise pattern in data with algorithms . This enable system to do future predictive analytics on the basis of correct labels . The reason why it is beneficial is it is being used to predict outcomes based on future inputs of data without any human interface .

Unsupervised Learning 

In unsupervised learning , although data is getting fed into the system but outputs are not labeled like in supervised learning . In this data is being observed and determined as per the predefined patterns , and data pattern is no where recognised . These system of ML concept is being used in social media platform wherein your friend who had been graduated from X university and have taken degree in Calculus , and you are being recommended on social media platform on the basis of your interest to follow this friend on the basis of demographic data .

Reinforcement Training 

Reinforcement training is very similar to unsupervised training . In this as the system is not labeled , so the system is left to create its own pattern . The reason why reinforcement is being categorised as different from unsupervised training is that when correct output is produced , the system is guided that the output is correct , thereby allowing system to explore full range of possibilities . If you are one of those Spotify user , you make thumbs up and thumbs down on the songs you like and dislike , and at this point they suggest you songs on the basis of music taste you are having by using concept of reinforcement training in ML .

Machine Learning Application 

Well , have you have thought of why machine learning is on high phase ? Well the answer being it is next generation solution in achieving concept of artificial intelligence , and is one of the most achieved things for developers . ML gives apps superpower to improve on the basis of users inputs , without intervention of developers . As it has ability to auto learn , it also saves the time of developers and helps enhancement of user experience by suggesting user what they want . When we talk about machine learning , there are two significant concept , the very first being image processing and the second one being predictive analysis .

Image Processing 

Image processing is being commonly used application of ML , which we all have experienced . Supervised learning concept is being used to recognise objects in image . In this we give turning to machine to recognise what we desire . For instance a machine is being trained to identify set of labels in image and once machine takes on the image will labels different objects in the image and provide their input accordingly . Apple’s Face ID feature is perfect explorer of this technology wherein apple tired to initially recognise your face from different angle and later on can analyse your face stored in data to perform multiple task 

Predictive Analysis 

The most popular category of ML is predictive analysis , which makes use of historical data to predict  or recommend your future data . No matters whether you are using Android phone or an iPhone , your phone now a days suggest you text which you are writing . That is predictive analysis of work . They usually recognises pattern of the words you use and then provide for suggestions for future responses .
Predictive analysis is being used across all industries to recommend customers what they desire of . E-commerce uses this technology to suggest buyer with commonly purchased item , wherein social media used this to recommend people whom to follow .

Machine learning is a very huge concept which has not been blasted in the market so well till now . Right from social media recommendation to recognition of your face , it is being used everywhere which has impacted our life in positive way . At the same time developers are also gaining better understanding of what users behaviour are and are able to deliver the things accordingly . ML also facilitates users with sense of next level security . In case you are willing to take your product to next level , task with one of Winklix product manager to identify ML and suggest best solution for you .