Category: Artificial Intelligence

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Comparison : Python Vs Node.JS

Comparison : Python Vs Node.JS

You must choose the right programming language as per your need and specification as every programming language has their own sets of pros and cons . While comparing Node.JS and Python , choosing the one among two is the main problem which can be address by right application developer.

In this blog we are going to differentiate between Node.JS and Python on the basis of upsides and downsides of the two , and then suggesting you to select the best that fits for you .

Why Choosing Right Technology Matters ? 

You can get plenty of recommendation from your techie friends , developers and other people for choosing the technology , but you won’t know which option is best suited for you ? 

Every tech framework and programming language has been developed to meet some particular needs of project . So don’t try to choose the technology just because it is popular one . You should choose the one on the basis of these factors : 

  • Budget 
  • Geography 
  • Type of product 
  • Type of project 

These can be various other factors , but you should take each feature of your project details into consideration during selection of technology for app development . While this article is limited to choosing between Python and Node.JS for backend development , we will restrict our discussion for this only and thereon benefits arising out of it .

Python Vs Node.JS

Before discussing any further , let us explain why we are actually comparing . Python is programming language while Node.JS is not . When tasing about Python it is Javascript , while on th other hand for Javascript , Node.JS is runtime language .

The basis differentiation you can say between Python and Node.JS is they both use similar language for both backend and front end while you are writhing in Node.JS. Now lets have a detailed discussion between the two .

1. Speed And Performance 

Node.JS

Node.JS is faster in performance while comparing with Python as Javascript code in Node.JS in interpreted in V8 engine . Node.JS used the code outside website browser . 

This as a result will ought to give better performance and will be called as more resource efficient . This at the same time allows you to utilise features that you are even not allowed to use in browser for instance say TCP sockets .

Node.JS also facilitates non-blocking event-driven architecture that is capable of handling many request at a time , which increase the overall speed of code application . Another major benefit being it has single module catching enabled which eliminates app loading time and make web app more responsive .

Python

Although both Javascript and Python both are slower in comparison with compiled languages live Java as they are interpreted languages . However Python is in comparison gives slower performance as request in this is more slowly processed .

Do not choose Python if you are willing to build application that are aiming at higher performance and speed and is involved in performing complex calculations .

2. Scalability 

Node.JS

You might be willing to attract lots of users to use your app without any hinderance . That is what scalability is all about . Scalability is concern about app’s ability to assist large number of people with absolutely no errors in performance .

Since Node.JS is built on asynchronous architecture in one thread it is highly scalable . Any web application built on Node.JS framework is highly scalable . Hiring a Node.JS developer who have deep expertise in this field will add on value in your project .

Python

Python does not support asynchronous programming , but contains some tools which provides scalability accomplishment .

Since Node.JS offers higher scalability , it wins in this race .

3. Architecture  

Node.JS

Node.JS facilities  asynchronous input and output due to its event driven environment . This procedure starts as soon as any event happens and that is the reason no procedure can hinder the thread . Thereby it is preferable for building web games and chat apps .

Python 

Python has been designed in different manner . Python is being used for developing event-driven and asynchronous apps by using specific tools . Modules like asyncio helps in writing asynchronous codes in Python . However asyncio has not been created specifically in Python and hence extra hands on these is required .

Here again , Node.JS winds the race .

4. Learning Curve   

Node.JS

If you are good friend of Javascript , then tan tana !! , you can easily learn Node.JS framework . It is because of its easy learning process , Node.JS is on top most position in the list of most famous framework and acquires 49 % of the share . 

Python 

Python as we all know is not as popular as Node.JS is , and hence its syntax is unknown to python developers . 

However it offers cleaner code writing and developer actually don’t have to write lot of code lines . In Python , some code lines can aid you reach similar outcomes as in Node.JS .

In addition to above , Python is old language which also facilities tons of documents sufficient for any developer to learn . 

As per stack overflow , Python is most preferred language . Full stack developers prefers using this simple language for app development .

So conclusion is Python is easier to learn in comparison with Node.JS .

5 . Syntax 

Node.JS

The Syntax of Node.JS is similar to Javascript and hence if you are familiar with javascript , you are not likely to face any hurdles with Node.JS

Python 

The syntax of Python is very easy to learn and at the same time is also free of curly bracket also . That is the reason why code is easier to debug and read . If you are a software techies , then python code is very much easier to learn and understand .

As a conclusion , Python wins in this case .

6 . Appropriate projects 

Node.JS

Web app development companies generally do not prefer to use this javascript framework for large projects as it lacks clear coding standards . But yes small project can be well developed using this framework .

Python 

Python can be well used in wide range of project that may involve numerical computations , web application , to network programming and machine learning . It is known to be perfect programming language to perform various tasks .

Python also facilitates different frameworks that can be used in building backend like Pyramids , Flask and Django . In addition to it , it also consist of frameworks for frontend such as PySide or Tkinter .

Python offers accurate coding which proves perfect for large projects . Hire python developers to develop your next big projects .

7. Extensibility 

Node.JS

It is easily customisable and integrated with different tools available in marketplace . It can be extend using built in APIs for building DNS ad HTTP server . It can also be easily integrated with Babel which can help you in frontend development .

Log.io is proven useful in error fixation and project monitoring , which tools like Jasmine is being used in unit testing . In case you want to do module building , process management and data migrations , you can easily use Webpack , PM2 and Migrat .

You can also expand your hands on Node.JS using Node.JS frameworks like Restify , Nest , Fastify , Koa , Meteor , Hapi , Express and more .

Python 

Many Python frameworks is available in marketplace . You can even integrate Python with Sublime Text editor that also provides some extra syntax extension and editing feature .

Python is known to be Robot framework for performance of test automation . Some of the web development frameworks are CherryPy , Web2Py , Pyramid , Flask and Django .

As a conclusion both Python and Node.JS are extensible easily .

8. Error Handling 

Node.JS

In general , errors are always part of development process , and therefore transparency and feasibility is identification of error is what all is required in programming . Node.JS is efficient in error handling which may arise at time of coding the applications .

Python

Python takes less time than even Node.JS in finding errors and bugs . And hence you will surely not waste your time in error rectification in both Node.JS and Python for your web app development .

9. Libraries  

Node.JS

NPM , the Node Package Manager is accountable for handling packages and libraries in Node.JS . It has large inventories of software libraries . NPM at the same time is very easy to learn for  developers  with proper documentation .

Python 

PIP , ie Pip installs Python Handles packages and libraries in Python . PIP is very reliable and very easy to learn for developers .

Therefore both Python and Node.JS wins in case of libraries .

10 .  Data and Memory Intensive Apps 

Node.JS

This is known to be best available framework to build run-tine-intensive apps . For instance you can easily use this technology to build chat functionality in app . Node.JS development companies build apps which can manage data steaming , queued points and proxy efficiently .

Node.JS is used to develop heavy traffic websites like eCommerce stores or building apps utilising 3D graphics .

Python

Due to its lower run time performance , it can not be used for real-time apps development . We also do not recommend to use Python for memory-intensive apps .

Thereby , Node.JS wins in this scenario .

11. Universality  

Node.JS

Node.JS is widely being used for backend coding of web apps . However you can also make use of Javascript for front end development . Node.JS is being used for building web apps , hybrid apps , desktop apps as well as IoT and Cloud solutions .

The best part is this cross platform framework aids developer in coding single desktop app which can be used on Mac , Linux and Windows , which in turns helps in lower cost for overall projects .

Python

Due to its full stack nature , it is being used for both frontend and backend development . You can also run Python program as it is cross platform like Node.JS .

Both Mac and Linux have Python previously being installed , but on Windows , you have to actually install Python interpreter by your own . Python is know to give best performance on both desktop and  web development , but at the same time is not recommended for mobile computing .

That is the reason Python is not being used in mobile apps development , but its demand in AI and IoT solutions is increasing day by day .

Therefore more Python and Node.JS is similar in terms of universality .

12. Community 

Node.JS

Node.JS has large number of community developers who are active on community . Since this is old language , developers from all over the world are used to of using this technology 

Python 

Python is known to be more mature than Node.JS and is open source as well . Its user community has very large number of contributors with expertise levels of experience .

As a conclusion , both Node.JS and Python has large communities . 

Conclusion 

It impossible to state which programming language to use and which one is better . Every one of them has their own advantage and disadvantage and language selection depends on the type of project you are looking to built and then take the decision in appropriate manner .

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IMPROVING SOFTWARE TESTING WITH AI

IMPROVING SOFTWARE TESTING WITH AI

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.

Conclusion

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.

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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 .