The story of the journey of data in the 21st century has been eventful. First, data made the shift from on-premise data centers to the cloud. Now, it is moving towards ‘edge’ points located close to the source of data generation. The dual foundations of Edge AI lie in Edge computing and Artificial Intelligence, two innovative technology trends that have taken the world of business by storm.
Edge computing brings processing, computation, and storage of data closer to where it is generated and collected instead of relying on moving it to a remote location such as a cloud. Edge computing reduces the dependency on bandwidth and curbs long-distance communication between servers and clients. Goodbye latency.
That’s a strong value proposition in itself. Now imagine combining that with the power of AI.
In this article, we will discuss the nitty-gritty of Edge AI and why it can become important for modern enterprises.
What is Edge AI?
Edge AI combines the goodness of Edge computing with Artificial Intelligence. With Edge AI, AI algorithms are locally executed on a, usually remote, hardware device using the data acquired from Edge computing. This device consists of a microprocessor and sensors. Since the data is obtained and processed without using any internet connection, the process happens in real-time and at the exact spot where it can have the most impact.
Edge AI helps in replacing the conventional processing residing on cloud-based data centers that demand heavy computing capacity by making it a part of the AI workflow on a local device.
Edge AI facilitates real-time operations and reduces power consumption as well as data costs since the device doesn’t need to be connected to the internet at all times.
While Edge AI is transforming the way IoT devices deliver value, it is also creating a monumental difference in the way enterprises do business. According to a report by Markets and Markets, the global Edge AI software market size is expected to grow to USD 1152 million in 2023.
As enterprises surge towards embracing ever-more contemporary digital technologies, entwining Edge and AI promises many benefits.
Let’s discuss why enterprises should be caring about Edge AI if they aren’t already.
Why Enterprises Should Care About Adopting Edge AI?
Predictive Maintenance: To ensure streamlined operations, consistent equipment performance, and reduced cost of repairs, predictive maintenance is extremely critical for enterprises. Today, businesses are investing heavily in digital technologies that help them identify loopholes and failures before they happen, and Edge AI enables this. Edge AI makes use of AI devices and sensors to detect anomalies, without depending on connection-based responses, helping to quickly respond and fix errors.
Increased Customer Engagement: Today’s customers seek bespoke product experiences that also offer personalization. Every app, website, product or business needs to be engaging enough for the customers to really hit the target. With Edge AI, connecting with customers in real-time is possible. Anything and everything they need is available in an instant, thanks to Edge AI (think smart assistants and offline apps). Edge AI curbs the problem of low latency. To the enterprise, it provides real-time insights that can further help in providing enhanced user experience.
Improved Data Analysis and Processing: In comparison to the centralized IoT platforms, processes powered by Edge AI enable faster data processing. With Edge AI, the analysis and processing of data is done in milliseconds since it doesn’t have to depend on an off-site cloud location. This improves the speed of response and automates decision-making.
Greater Security and Privacy: Security and privacy are key concerns for enterprises and Edge AI helps in addressing those challenges. Edge AI-powered systems can trigger alerts in real-time based on behavioral analysis, help to detect phishing attacks, drive password protection and authentication, and examine network traffic patterns to protect corporate networks. Here’s a great resource to dip into for more on How AI Can Help Combat Security Challenges?
Conclusion:
According to a report by Tactica, the shipment of AI edge devices is expected to grow up to 2.6 billion units by 2025. Several research reports show that AI is becoming more mainstream. As both these technologies mature, Edge AI looks set to enter diverse industries through an array of use-cases.This is the time for visionary enterprises to get serious if they want to be at the vanguard of an exciting new movement!
Today, technology is on the verge of taking over life and work. It is also altering the way we communicate, interact and the way we conduct our day to day transactions. Sophisticated technology is also redefining the way business functions. It may not be wrong to say that we have come to be dependent on technology. That is a bit of a blessing as well as a curse.
The critical question is,how equipped are we to combat the security challenges that come have also been enabled by the rise of technology? In fact, are we really aware of the potential threats created by the digital revolution and can technology itself combat these security challenges? Amid the various solutions that are being talked of as a way to combat some of these challenges, AI is showing some remarkable results in protecting data and privacy of the users with its incredible versatility. Be it network security, behavioral analytics, phishing detection or vulnerability issues; AI is stepping in to deal with it all successfully.
Today, the innovation edge of AI provides some of the best solutions that can combat the security challenges.
Let us find out how!
Alerts Based on Behavioral Analysis: The behavior-analytics ability of AI can help curb a slew of cybercrimes. Every user has a signature – a specific distinctive pattern of online behavior. This behavior is determined by factors such as the devices that they usually use to login, the geography, IP addresses, typical time slots, their search history,etc. AI learns these behavioral patterns and records them to reproduce it while creating security alerts. An AI-enabled solution alerts the user immediately if there is any unusual activity such as login through an unusual device in a different geographical area, mismatched IP address, etc. So, if there’s any malintent lurking around, AI immediately brings it to the notice of the user and even blocks certain elements of the user’s online platforms.
Protecting Against Phishing- Almost 1 out of 99 emails is a phishing attack. Phishing has, unfortunately,become quite prevalent today. It is a huge threat to organizations and individuals alike. A combination of AI and Machine Learning can play a crucial role in preventing these phishing attempts to avoid significant damages. AI can scan any phishing activity without being bound by any geographical barriers and can easily detect these wannabe scammers. AI can easily differentiate between a fake website and an authentic website. In fact, AI has been found to have the ability to detect close to 10,000 active phishing sites all over the world. AI is not only capable of creating alerts but is also designed to act immediately to avoid damage.
Password Protection and Authentication: Passwords are often the last line of defense between cyber criminals and sensitive user-accounts. With users adopting simple passwords and also similar passwords across different accounts, hackers can easily isolate vulnerabilities and slip through a loophole to steal data easily. Biometric identification has become a strong wall between users and hackers;however, it can still be broken down by creative hackers. Even technology such as face recognition has not been fully successful in reducing cyber security attacks. Therefore, developers are now using AI in biometrics to make it stronger and more reliable. Apple’s facial recognition technology in the iPhone is a good example of how AI can protect fiercely, where other protection measures may fail.
Protecting Corporate Networks: The security policies of a company can play a big role in protecting data.These security policies and a clear understanding of the network topography can determine the robustness of network security. Many a time,loop holes exist in these policies or gaps may creep into their implementation. Also, vulnerabilities in network architecture design can make the data and other assets of an organization vulnerable to potential intrusions and mala fide activity. They can also expose the network to malware and other attacks. AI studies network traffic patterns to enable the organization to configure and design the security policies accordingly. AI can examine the network design as well as the implementation to identify possible vulnerabilities that need to be fixed. This helps to direct the time and effort of the organization productively and effectively.
Conclusion-
Sadly, it is impossible to eliminate chinks in the security armor at all levels. Security threats and breaches are now a part and parcel of the digital revolution. Cyber-attacks are commonplace as unsavory elements try to exploit vulnerabilities for financial gain or even just for a cheap thrill. Every person’s and every corporate’s data is vulnerable. In that scenario, it is incredibly important to adopt comprehensive security measures today.In the race to stay ahead of the cyber-malcontents, AI looks like a promising option with its versatility and capability of mitigating risks and alerting users to possible threats.
AI (Artificial Intelligence) is the new human-machine that understands customers better than anyone did before! The aim is nothing less than establishing a bridge between technology and its users by being more human-like. AI is being built to make life and businesses easy and convenient, and;eCommerce is one area that’s already feeling the AI magic!
A report by Boston Consulting Group (BCG) shows those retailers who have implemented personalization strategies, gain almost 6 to 10%; a rate that is two to three times faster than other retailers who haven’t adopted the same strategy.
In fact, AI is predicted to boost profitability rates by almost 59% in the wholesale and retail industries by the year 2035!
Personalization is the key to the enormous popularity of AI amid eCommerce.
AI brings with it versatility, convenience, easier transactions and much more! Amazon is an exemplary example of AI revolutionizing eCommerce. With its brilliant AI-driven support; Amazon has created a competitive advantage in the market based on intelligent personalization and uber-relevant customer service.
In line with the hype, AI has become the newest sales-magnet. One that redefines marketing goals and strategies and frees up manual time and efforts too. Online businesses are forging ahead with confidence in the power of AI!
Let us see 5 areas where eCommerce can gain from Artificial Intelligence!
Increased Personalization:
Gone are the days when the sales person had to personally persuade you to make a purchase. In the online world, AI is the new salesperson, much more empathetic and strategic. AI is intuitive enough to read your mind and deliver what you want within the committed time!
It just takes a robotic human-like chatbot to empathetically enquire about your needs and answer all your questions smartly and elegantly, offering equally interesting and varied solutions! The AI presents the right information at the apt stage of the buyer journey to drive the sale forward.
AI has enabled eCommerce merchants to know the preferences, likes, and dislikes of customers to serve them exactly with what they need.
Smart Purchasing:
AI helps customers to find what they need, even if they don’t quite know it yet!Smart assessments of the customer’s needs, the best-bundled offerings, and custom price points power a shopping cart that contains everything the customer could need. This pro-activeness enables the customer to make a quick purchase decision. AI has created the era of smart purchasing where the technology drives intuitive purchasing options. Of course, that’s not the only purchasing where AI can help. Purchase-planning by the business is extremely critical to success in eCommerce. With intelligent systems and machines,they can have enough stocks to dispatch on time, making the necessary goods available to the customers at the right time.AI-enabled demand-forecasting makes purchase-planning for the eCommerce business easier by reliably predicting turnover, seasonal changes, and product trends.
Enhanced User Experience:
AI enables you to know your customers so well that you can tailor not just the products around their preferences, but also the messaging around their thoughts and sentiments. This sends out a clear message that ‘you care while you sell.’
Enhanced customer experience is built around effective browsing and searching You should ask the questions, why a customer would prefer my website over others, what it is that can make my customer come back again and again. AI tools can also predict future purchase needs, follow-up and bring them around with the e-mailers and other digital real-time advertising.
Customer Relationship Management: A neglected customer may never come back to your eCommerce website. Today, it is tougher than everto create loyal followers and customers, or even retain loyal customers for that matter! AI consumes enormous amounts of data, processes it and analyses it to deliver insights around effective engagement alternatives for the customers that need it. This means knowing which customers to target with such strategies as well as creating effective retention strategies.
Bigger Sales and Profits:
It goes without saying that enhanced customer management, personalized marketing, and better outreach naturally lead to higher sales and profits in the eCommerce business. Convenience and speed matter to most customers. Virtual assistants and chatbots that help customers make the right decisions quickly help deliver exactly that. AI delivers profits through quick, action-oriented automated that makes customers want to come back and purchase more. In essence, AI saves time and effort for both, the customer as well as the eCommerce merchant.
Other Benefits of AI Technology:
All the elements that contribute to successful eCommerce business are linked and don’t really work in isolation. Along with a personalized online ambiance that enhances the customer relationship, here are some more advantages of AI in eCommerce.
Inventory Management:
AI’s predictive analytics helps shop-owners to know the present and future demands of the market and the customers. They can now accurately predict the real-time and future inventory needs and plan to make the goods available on time.
Image Classification:
AI is certainly changing the game with its ability to classify, interpret, and understand images. Image classification also gives the advantage of suggesting alternative and similar-looking goods in the form of images to provide customers meaningful options to choose from.
Enhanced Security:
Probably one of the most crucial aspects of online shopping is security. While customers and online users are worried about online fraudsters and fake marketers trying to get information; AI is coming to the rescue. AI solutions that detect patterns and learn from credit card transactions can detect advanced cybercrime techniques and also identify fake and genuine reviews.
Conclusion:
AI has the potential to drive an enormous impact for the eCommerce industry. Right from personalization, to recommending products, easier customer management, and enhanced user experience, the dynamics of online shopping have changed dramatically. Undoubtedly, the eCommerce brands that embrace AI have an advantage over their competitors in getting ahead of the curve to create that ‘wow’ factor for their customers.
Distributed Cloud Computing has already made it to Gartner’s top ten strategic technology trends of 2020. New-age business processes are being designed to make use of distributed cloud infrastructure, applications,and databases to address modern workflows and their requirements. With the advent of advanced technologies such as distributed cloud computing, companies have accelerated the move away from their traditional ways of thinking and are now open to making the cloud central to their business strategies.
To accommodate a huge number of customers and growing volumes of data, traditional companies are now making the strategic shift by embracing the technology necessary for them to become cloud-driven companies. Clearly, cloud-native giants such as Facebook and Google have not only successfully adopted cloud, but they are visibly reaping the benefits of their cloud move. The sense is that to gain a competitive advantage in the market, companies have to scale-up their cloud strategy. And those who won’t make the move will stagnate, or worse.
This is a paradigm shift for many,which also means that there are a lot of questions to be answered before and after they adopt these technologies.
But the most fundamental question is, should companies care about distributed cloud?How can it impact their business!
Understanding Distributed Cloud Computing– With distributed cloud computing,you get computation, storage, and networking in a micro-cloud located outside the centralized cloud. Edge computing is a very good example of distributed cloud technology. Distributed cloud computing establishes computing, closer to the end-user,thus enhancing security. This distributed cloud model brings more agility, thereby giving enterprises better business outcomes.
Personalize Cloud Strategy– The biggest advantage of the distributed cloud is that it allows companies to diversify and still personalize their cloud strategy. The distributed cloud also allows companies to pick and choose the various capabilities of different cloud providers and solutions to align with their specific business needs.What makes distributed cloud so popular is that it gives immense flexibility to companies to be able to pick different cloud providers, solutions, and infrastructures depending upon the type of applications they want to address. So, while companies want to make this shift, they also need to prepare. They will need to make changes to the existing infrastructure to create robust connectivity between different modules of the cloud environment.
Prioritize Business intelligence– Talking about impactful applications, enterprises now want to weave business intelligence into their IT infrastructure and distributed cloud computing is supporting this move. They are focused on what more they can do with the data available to them. The distributed cloud allows the scaling of data storage and processing capabilities for companies. This is allowing companies to adopt and take advantage of other advanced technologies such as AI, Machine Learning, etc. This integration is helping companies to leverage business intelligence and predictive insights for enhanced profitability. Enterprises can now go beyond capturing their operational data and embed intelligence into their core operations, bringing in more agility and productivity. This intelligence also facilitates capturing interactions between the company and the customers to analyze and evaluate products, services, and the effectiveness of overall business functions. Going forward, the cloud is also likely to play a bigger role in shaping the overall IT strategy of companies. Offering a more collaborative IT structure; it can help bring together operations data and customer data to create a better product experience and an enhanced workplace experience too.
Data and enhanced Customer experience– While the cloud facilitates data-processing in real-time, it also enables functions that manage that data. Better data management can lead to a meaningful rationalization of the data foundation. Clean, current, accurate, consistent, and relevant data can drive strategies for enhanced customer experience. Data management helps drive agility in responding to the needs of the customer. It can help in understanding the various preferences of the customers, enhance response methods, and power the building of long-term business relations built on a more customized solutions’ approach.
Distributed Cloud and Security– While enterprises are looking at distributed cloud computing as a boon, some of the companies that still hesitate to adopt cloud are doing so due to some perceived security issues. Distributed cloud has data distributed over a large number of machines and networks, wireless sensors, mobile devices, etc. This scattered data can mean higher risks of security breaches and can make data security in the cloud more complicated than centralized systems. However, a lot depends upon the individual company data compliance stance, IT regulations, governance controls, and data security administration policies.Companies that can address security concerns with well-managed cloud computing infrastructure stand to gain, while still staying secure.
Conclusion:
IT is becoming the backbone of business performance. But businesses are looking at solutions that can simplify the complex nature of their IT infrastructure. They want to make IT incredibly manageable,driving better business outcomes. The distributed cloud makes it easy to integrate new technologies within a seemingly simpler IT infrastructure.
While enterprises are already looking beyond just capturing operational data; this method of cloud computing offers a lot more in terms of flexibility, convenience, cost control, and, most importantly,nurturing specific business applications like enterprise intelligence! There’s no doubt that 2020 will be the year where enterprises will dive into the distributed cloud. The business case is just too compelling.
What is it that makes an Amazon or Netflix so popular among people?
Why is it that we go to a seemingly interesting website and then quickly leave it?
The answer may lie in the experience. You go to the Netflix app because you want to be entertained. You open the Amazon website because you want to compare prices and order a few things. Now, imagine if the design of the website or the app was confusing. What if Netflix did not give you personalized recommendations? What if Amazon did not categorize its items? You might waste a lot of time looking for what you want and might end up exiting the website or the app within a few seconds. For a digital-first company, that spells bad news.
Gone are those days when customer experience was just a way to stand apart from the competitors. Today, for apps and for software products, it is a necessity to survive in the hyper-competitive market. 86% of customers today are willing to pay a premium for excellent customer experience. A study by Walker suggests that in 2020, customer experience will overtake price and product as the key differentiator.
So, how do you create an experience that will make your customers stay on your platform?
To begin with, how do you even determine what good experience means to your customer?
The answer may lie in big data.
How has Big Data Helped Companies to Improve Customer Experience?
Companies have increasingly started relying on data to find ways to make the customer’s experience frictionless.
However, with terabytes and zettabytes of data generated every year, companies find it hard to collect, analyze, and interpret the data correctly.That’s when they use technology like big data.
Big data extracts information from large data sets systematically to enable businesses to make informed decisions.
Here’s how.
Helps in Understanding the Behavior of the Customers: When you operate in a physical retail space, it is easier to observe the behavior of people and provide them what they need. But how do you emulate the same thing online? That’s where big data helps. With big data and analytics, you can find out how people interact with your product or website, how they prefer to engage, what prevents them from taking action? You can mine insights from customer support queries and online forum responses about what irks the customer? What features are they looking for? Understanding the behavior of the customer will enable you to make improvements to your product in line with the needs of the customer.
Helps in Rectifying Customer Pain Points: Taking the previous point further, big data helps you to rectify the issues customers face while using your product. Let’s take Amazon for instance. What made Amazon introduce the 1-click ordering feature on their mobile app? It was not a random decision. Amazon realized that most customers backed out on having to enter all their information manually during the checkout process. With the 1-click option, the users can place an order without entering any details. This saves their time and increases conversions for Amazon. Once you gather information on what prevents your customers from deriving the necessary value from your product, your UX and UI designers can rectify the design or you can take the call to add a much-needed feature.
Helps in Improving Visualization of the Website or App: To design a great product, you have to determine what you want the customers to do and then create the interface such that it helps them do that. Visualizing such a design might look simple, but if you want the design to be effective, you have to turn to big data. Let’s take Instagram’s example. Data revealed to the widely popular photo-sharing app that people were unable to find relevant images and videos on their feeds. So, they altered the feed from reverse-sequential order to display posts that they thought people would like to see, like, and share. This helped them to customize the data feed for their users and led to more popularity. The data collected through photos and videos let visitors discover new interests relevant to them. So, if you are planning to revamp the design of your product, read the data you collect from your customers and mine those insights to improve the usability.
Removes all guesswork and leads to data-backed experiments: Every product change will cost money for your company. It could also earn you bouquets or brickbats depending upon how your customers react to the change. So, if you want to experiment with your UX or UI, conduct data-backed experiments. Conduct usability tests on a small batch of loyal users to know their feedback on the changes and use those insights to improve or modify the design. Turn to predictive analytics to get a sense of what could happen. This saves time and money and helps you focus on strategies that have a greater chance of working.
Creates personalized interface for every user: Think of the Amazon homepage showing recommendations of products to buy or the personalized content recommendations by Netflix. Conditioned by the experience provided by apps like these, even enterprise and business users are demanding greater levels of customization. And the app and product experience can be so much more meaningful to your users too,thanks to big data. Analytics helps you bring that relevance in your products that encourages higher engagement.
Conclusion:
Until a few years ago, software products never thought of combining art and science to improve the customer experience. Today, customer experience has become more data-focused and customer-centric. And all credit for this significant shift must go to big data and analytics. Analytics has enabled companies to adopt science to create personalized experiences that make every customer feel special.
The past few years ushered us into the era of mobile applications, where mobile apps have become an integral part of our everyday life. Be it Netflix, Facebook, Instagram, Uber, Skype, or more mobile applications are trending excessively in this day and age. However, with this increasing dependency on mobile applications, the need for niche technologies, frameworks, and platforms is also rapidly increasing, giving way to the advent of new frameworks and platforms that allow developers to create cross-platform apps that are suitable for all platforms.
From simplifying the app development process to making them efficient, these frameworks are helping reduce the complexity of mobile application development and are hence trending among developers. Among these, Flutter and React Native are the two most popular Cross-platform Mobile App Development Frameworks that are enabling developers to create cross-platform mobile applications that work seamlessly across various platforms and devices. Moreover, these two frameworks are competing against each other to prove their worth, making Flutter Vs React Native the most trending topics of the year.
So, let’s try to determine, “What is the difference between Flutter and React Native?” and answer the important question, “Will Flutter replace React Native?”. But before we delve deep intothis discussion on React Native vs Flutter, it is important that we understand the need for cross-platform development frameworks.
The Need for Cross-Platform Mobile Development Framework:
Nowadays, Android and iOS are two of the most widely used mobile platforms, with a completely different application development process. Android requires developers with extensive knowledge of Java or Kotlin, whereas iOS needs developers well versed in Swift programming language, making the development process expensive and time-consuming.
Cross-platform app development came into the inception to overcome this drawback and has become the need of the hour. Industries are using frameworks like React Native, Flutter, Xamarin, PhoneGap, and more, to create cross-platform applications, as they enable a single team to create apps with a single code base that works on multiple operating systems (OS), like iOS and Android. Due to this, most of the applications developed today are either cross-platform or hybrid and can run seamlessly on iOS and Android. Other advantages offered by cross-platform mobile application development frameworks are:
It offers UX uniformity.
Ideal for prototyping.
Requires one team to create one product for two or more platforms.
The code can be reused across platforms.
Quicker Development.
Easier Implementation.
Now that we know the reason for the shift from native app development to cross-platform and hybrid app development, let’s compare the two important cross-platform mobile app development technology.
React Native: Understanding the Basics:
Launched by Facebook in 2015, React Native is an open-source JavaScript framework built upon the React library and used to build natively rendering, mobile applications for iOS and Android. One of the most reliable and popular JavaScript frameworks used for developing mobile apps, React Native combines native components with React, the best-in-class JavaScript library for building the User Interface (UI). React Native enables developers to create react native apps for iOS, Android, Windows, and Linux, though the latter two require dependency managers like HomeBrew package manager.
Used by Facebook, Instagram, Airbnb, Skype, Tesla, Walmart, etc. and backed up by a huge developer community, React Native popularity is tremendous due to its ability to build applications efficiently, in less time as well as its use of Node Package Manager (NPM) for installation, excellent UI rendering, GPU oriented application development, seamless integration and quick load time, etc. Additionally, its features like platform-specific code and hot reload make it a common choice for developers for mobile application development.
What is Flutter?
Flutter, one of the biggest React Native competitors, is a free and open-source mobile UI framework created by Google and released in 2017. Though new to the spectrum of mobile application development, it is gaining popularity and momentum among web and mobile developers for creating native applications, with a single codebase. In short, with Flutter, developers can use one programming language and codebase to create two different apps for different platforms.
Unlike React Native, Flutter does not use JavaScript, but rather a less known programming language Dart, which was created by Google in 2011. Dart programming language is focused on front-end development and can be used to create applications for both web and mobile. Though most of the systems are implemented in Dart, the factor that differentiates Flutter from other mobile application SDK is that it has a thin layer of C++ or C. Moreover, it is supported on Android Studio, IntelliJ Idea & Visual studio code.
Flutter Vs. React Native: Key Points
From being open-sourced, fast and free to offering excellent UI support and native-like experience, React Native and Flutter, two major competitors offering cross-platform solutions, share various similarities. However, there are certain aspects of these two frameworks that make one framework superior from the other, which will be highlighted in the following comparison:
Programming Languages: Flutter Dart vs React Native JavaScript
A major advantage of using cross-platform mobile app development frameworks is it allows developers to create applications for both iOS and Android using a single programming language.
Flutter uses Dart programming language to create a Flutter app. Though new for mobile application developers, Dart is easy-to-use for developers experienced in different OOP languages such as Java and C++.
React Native: Uses JavaScript to build cross-platform apps. Extremely popular among developers, this programming language helps web developers build apps with little training and hence is a winner compared to Flutter.
User Interface:
As React Native is purely focused on UI design, it has a large number of React UI components that are more extensive than that of Flutter. This is because application development with React Native is highly based on native components, whereas Flutter works flawlessly with the owner widget sets. One advantage of these widgets is that they prevent the developer from being dependent on third-party UI libraries.
Development Time:
Development time and process are the two most critical aspects that need consideration during mobile application development. Flutter, prominently known for fast and simple development, is lauded for its hot reload feature, which enables developers to instantly view changes and implement modifications. Moreover, it provides a full suite of extensible components that are built from scratch. Whereas, React Native, though popular, relies heavily on third-party libraries, which becomes makes the process comparatively slower.
React Native Vs. Flutter Performance:
React Native, though popular for providing high-quality user experience, is considered less suitable for application development because of its architecture, which impacts its performance and makes it slower. Whereas, Flutter’s ability to reuse the same code for creating applications for different platforms as well as its use of widgets and GPU to render it on apps on the screen helps create apps with best-in-class performance and speed.
Documentation & Toolkit:
Google, like always, provides clear, structured, and in-depth documentation for their products, with Flutter being no exception. From installation to widgets, testing, more Flutter offers proper documentation for all and is backed by the Flutter team. React Native lags behind in this aspect, as it has a poorly maintained and unclear documentation, with not much explained like installation and configuration setup. Moreover, it lacks official documentation for Continuous Integration & Continuous Delivery (CI/CD).
Technical Architecture:
Another important aspect that needs consideration when comparing React Native and Flutter is technical architecture.
Flux Architecture:
As React Native relies heavily on JS runtime environment architecture known as JavaScript bridge, it uses Facebook’s Flux architecture, which helps it to communicate with native modules. This though beneficial results in poor performance.
Skia:
On the other hand, Flutter uses Dart Framework, which has most of the components inbuilt and does not require JavaScript bridge to communicate with the native UI component. Moreover, it further uses the Skia C++ engine which consists of all the protocols, compositions, and channels needed to develop a mobile app. This independency of Flutter makes its architecture more beneficial than React Native.
DevOps and CI/CD Support:
To ensure an application receives continuous feedback and is not released with bugs, it crucial to adopt Continuous Integration and Continuous Delivery practices. This is ensured by Flutter, in its section on Continuous Integration and Testing, where its rich Command Line Interface (CLI) allows easy set up on CI/CD services strong CLI tools, whereas, React Native does not provide any instructions on CI/CD practices.
Installation:
The installation process with Flutter is more straightforward, with the added advantage of automated checkup of system problems. On the other hand, react-native lacks a streamlining setup and configuration.
Development Tools:
Some of the tools used in both React Native and Flutter mobile application development are:
Flutter:
Flutter SDK.
DevTools.
Hot Reload.
React:
Expo.
Redux.
Flow.
Ignite.
React Navigation.
Conclusion:
React Native is currently ruling the spectrum of cross-platform mobile application development, however, there is no doubt that Flutter is working hard to prove its worth and is slowly taking over the future mobile application development, by making the development more streamlined, introduction of new features and functionalities, as well as by saving the developer time and effort. However, it is still too soon to answer to questions like “Is Flutter better than React Native?”, as Flutter is still climbing the ladder to achieve the popularity and reliability that React Native is currently enjoying.
Our previous article touched upon the key features of AngularJS and ReactJS. But it’s been a while since then. Technology never stands still. There is now a considerable debate over which of those is better suited for front-end development. And here we are again with an updated account of what differentiates the two technologies, and which could be the better option for specific situations.
Web development is progressing at lightning speed. Something that was hot and relevant in 2017 may be seen as archaic today. Today, users have even more control and power over what they want. Product and app companies are being forced to frantically try and stay on-trend while meeting those growing demands.
In the software and mobile app universe, two technologies are being spoken of far and wide- Angular and ReactJS.
While Angular is an MVC framework, React is a JS library that handles only the Views in an MVC framework. Despite, or perhaps because of, some foundational differences between the two technologies, there are several debates about the capabilities and preference of one over another.
Angular handles many functionalities out of the box and is centered around the concept of an application. React is more lightweight and does not handle quite as many functionalities out of the box. It comes with the concept of components, each with their own properties.
With that snippet of an introduction of both technologies, let’s try to (again) nail down the differences between them to help you pick one best suited to your unique needs.
Angular vs. ReactJS
With several out-of-the-box functions, Angular can help get you started quickly without feeling overwhelmed or intimidated by choices. Usually, developers feel at home quicker and switch between roles more comfortably with Angular.
For the web app-focused folks, while all Javascript frameworks have SEO capabilities, ReactJS does a particularly great job here. Run ReactJS on the server and the virtual DOM will be returned to your browser as a consistent web page.
Earlier, Angular was built to offer ease of software development, particularly through the use of modules. In that light, Angular also provides ease of testing, another advantage of the technology.
ReactJS Native is more focused around UI, unlike Angular. It allows for a more responsive interface with Javascript communications between the Native environment of a device and the framework. Therefore, it impacts the app’s load time and keeps it running seamlessly without interference.
Use cases of Angular vs. ReactJS
Use ReactJS when there is a lot of dynamic content in your software or applications. Top brands such as Facebook, Netflix, Uber, Dropbox, PayPal, Flipkart, and Instagram prefer ReactJS to drive their apps for this dynamic nature.
ReactJS could also be the choice for you when you plan on expanding the functionality of your mobile app in the near future.
On the other hand, choose Angular if you are just starting with app development, and want to be done quickly. Angular may also be the right choice out of the two when you are looking for a robust and well-maintained framework. Angular may work better when you have a team of experienced developers with a good hold on TypeScript.
Performance of Angular vs. ReactJS
React is racing ahead in the popularity charts for its fantastic rendering speed. The technology derives its name from its ability to react to change with minimal delay. In response, Angular 2 has been tried to improve its performance by modifying its ‘change detection’ algorithm.
When it comes to smaller applications, both technologies stand neck to neck. However, when apps get more complex and larger in size, React outperforms Angular. React can also be easily combined with Redux or Flux to build bigger applications.
React developers also have the benefit of a built-in virtual DOM feature that allows a server to save a light DOM tree, leading to less loading time on browsers and high performance.
The Learning Curve of Angular vs. ReactJS
One of the distinguishing factors of ReactJS is that it is easy for anyone to learn. Since it is purely based on JavaScript, even newbies can familiarize themselves with the technology in no time. The learning curve associated with React is shallower and better suits beginning developers while Angular comes with a steep learning curve. This is essentially because developers are expected to be well-versed with the additional dependency of TypeScript, a statically typed programming language from Microsoft.
Why Choose Angular vs. ReactJS?
Choose Angular for its component structure, where you can utilize its components with several frameworks without having to stretch. Angular binding permits you to build a simple-to-maintain, logical connection between the model and the data view. It also allows for smooth testing and the flexibility of choosing your preferred environment for app development.
Choose ReactJS for its Virtual DOM, which increases the speed of the framework. Also, for its Simple State Machine components that allow you to modify the state of an object and apply all updates with ease. Developers also love the fact that the view is not disparate from the logic in ReactJS. That, and it’s fast to learn.
While there is no black and white here, the situation your product or app is in will help you make an educated decision on whether Angular or ReactJS would better suit your needs. For further assistance, reach out to us, and we will help you define the best roadmap for your application development needs.
Presumably, everyone here who has developed products knows that regression testing is done to validate the existing code after a change in software. Unlike most other testing, it validates that nothing got broken in the already existing functionality of the software product even as changes were made to other parts. In a nutshell, the aim is to confirm that the product isn’t affected by the addition of any new features or bug fixes. Often, older test cases are, re-executed for reassurance that there were no ill-effects of changes.
Regression testing is necessary for all product development where the product is evolving, that is, in effect for all products!
Which Brings is to Agile Software Development?
The Agile method calls for rapid product iterations and frequent releases. Obviously, this includes shorter and more frequent testing cycles. This is to ensure that the quality of the output of the sprints is intact whenever the software is released. These constant churns call for a massive focus on regression testing.
A sound regression testing strategy mainly helps the teams focus on new functionalities and maintain stability as the product increments take place. It makes sure that the earlier release and the new code are both in-sync. This is how the software’s functionality, quality, and performance remain intact even after going through several modifications.
To put things into perspective – the Agile method is all about iterative development and regression testing is all about focusing on the effects that occur due to that iterative new development.
What Makes Regression Testing Special in Agile Development?
Helps Identify Issues Early– One of the ways in which Agile teams build their regression testing strategy is to identify the improvements or the error-prone areas and gather all the test cases to execute for those cases. This preparation helps them gear up for the accelerated tests and also, prioritize the test cases. This way they can target the product areas that need more focus on quality. Additionally, by detecting defects early in the development cycle, regression testing can help reduce excessive rework. This helps release the product on time.
Facilitates Localized Changes – Regression testing makes it possible for development teams to confidently carry out localized changes to the software or sometimes, even for bigger changes. The teams mainly focus on the functionality that they planned for the sprint secure in the knowledge that the regression tests will highlight the areas that are affected by the most recent changes across the codebase.
Business Functionality Continuity – Since regression testing usually takes into consideration various aspects of the business functions, it can cover the entire system. The aim is to run a series of similar tests repeatedly over a period of time in which the results should remain stable. For each sprint, this helps test new functionality and it makes sure that the entire system continues to work in an integrated manner and the business functionality continues in the long run.
Errors Are Reduced to a Large Extent – The thing with an Agile development environment is that there is a reduced scope for errors during the accelerated release cycles. The series of regression tests at each level of the release ensures that the product is robust and resistant to bugs. This helps in enhancing the software’s stability and improves its overall quality.
Offers Scope to Add Better Functionalities – Introducing new functionalities in any application can be time-consuming because there are several aspects that need to be taken into consideration. This process becomes less cumbersome with Agile development, which can boost gradual changes. Regression tests amp up the power of the methodology by giving the scope of introducing several functionalities in seamlessly.
Quicker Turnaround – There are multiple tools for regression testing. It’s also possible to automate significant portions of the regression testing given the repetitive nature of the tests. This offers the Agile development team faster feedback. They can achieve faster turnarounds and can accelerate releases confidently.
To Sum Up:
Regression testing is a staple while developing a well-integrated, robust software as it evolves. In the accelerated Agile environment, it helps ensure that any newly developed sprint has no adverse effect on the existing code or functionality of the business. Furthermore, a carefully considered regression testing strategy helps the Agile teams be confident that every feature in the software is in perfect condition with all the updates and fixes required. It’s the insurance policy that Agile product development teams need.
After test automation, AI-based testing looks like the obvious next step. Here’s how things have rolled out in the software testing space:
Traditionally, manual testing has always had a role to play, because no software is produced sans bugs. Even with all the tools available, a key part of the process is handled manually by specialized testers.
Over time, test automation took root. In several cases, test automation is the only feasible approach when you need to run a large number of test cases, fast and with high efficiency.
AI-enabled testing is making test automation smarter by using quantities of data. QA engineers can feed historical data into algorithms to increase detection rates, implement automated code reviews, and automatically generate test cases.
Let’s take an overview of what AI can do in Software Testing.
The Potential of AI in Software Testing:
As organizations aim for continuous delivery and faster software development cycles, AI-led testing will become a more established part of quality assurance. When considering only software testing tasks, there are several tasks that quality Assurance engineers perform multiple times. Automating them can drive huge increases in productivity and efficiency.
In addition to the repetitive tasks, there are also several tasks that are similar in nature, which, if automated, will make the life of a software tester easier. And AI can help identify such fit cases for automation. For instance, the automated UI test cases that fail every time we make a change in a UI element’s name can be fixed by changing the name of an element in the test automation tool.
Artificial Intelligence has several use cases in software testing, including test case execution, test planning, automation of workflows, and maintenance of test cases when there are changes in the code.
But what are the limitations?
Why AI will not take over entire QA phases?
Even though Artificial Intelligence holds strong promise for testing, it will be hard for mere technology to completely take over.
Humans need to oversee AI:
Artificial Intelligence can’t (yet) function on its own without human interference. Until then, organizations need human specialists to create the AI and to oversee operational aspects that are automated with AI. In short manual testers will always be a part of the testing strategy to ensure bug-free software.
AI is not as sophisticated as human logic:
While there have been significant advancements in Artificial Intelligence, it does not beat the logic, intuitiveness, and empathy inherent in humans. AI will bring about more impactful change in the way it assists software testers to help them perform their tasks with more accuracy, precision, and efficiency. But for all tasks that need more creativity, intuitive decision making, and user-focused assessments, it may have to be human software testers who hold the fort. For a while at least!
AI can’t, and never will, eliminate the need for humans in Testing:
Organizations can use AI-based testing tools to cover the basics of software testing, and easily uncover defects by auto-generating test cases and executing them for desktop or mobile. However, such an approach isn’t feasible when you need to assess a complex software product with various functions and features to test. Experienced software QA engineers bring a wealth of insights to the table that goes beyond the data. They can make the decisions that must be made even when data doesn’t exist. When a new feature is being implemented, AI may struggle to find enough solid data to define the way forward. Experienced software testers may be better suited to such situations where they can make intuitive leaps based on nothing more than their judgment.
Functions in Software Testing that can’t be entirely trusted to AI:
AI can seamlessly help with tasks that are repetitive in nature and have been done before. But, even if we leverage AI to its full potential, there are jobs within QA that demand human assistance.
Documentation Review – Comprehensively learning about the ins and outs of a software system and determining the length and breadth of testing required in it is something better trusted to a human.
Creating Tests for Complex Scenarios – Complex test cases that span several features within a software solution may be better done by a QA tester.
UX Testing – User experience can be tested and assured only when a user navigates the software or application. How something looks to the users and, more importantly, how it feels to them, is a task beyond the likely capabilities of AI.
Just like automation aims at reducing manual labor by addressing monotonous tasks, AI-led QA minimizes repetitive work with added intelligence by taking it up a notch up.
This means QA engineers should keep doing what they do best. However, it will help QA testers to familiarize themselves with technologies AI to advance their career when these tools become commonplace. The truth is that AI is making a stand, but we still need diligent, creative, and expert QA engineers on our product development teams.
Amazon uses it to deploy new software to production at an average of every 11.6 seconds!
Netflix uses it to deploy web images into its web-based platform. They have even automated monitoring wherein they ensure that in the event of a failure in implementing the images, the new images are rolled back, and the traffic is rerouted to the old version.
NASA, on the other hand, used it to analyze data collected from the Mars Rover Curiosity.
It’s become such that every organization that focuses on quick deployments of software and faster go-to-market uses DevOps.
Statista reveals that 17% of enterprises had fully embraced DevOps in 2018 as compared to 10% in 2017.
Given the advantages, these numbers will only grow every year as companies transition from the waterfall approaches to develop fast, fail quickly, and move ahead on the principles of the agile approach.
But for DevOps to deliver to its fullest potential, companies need to move from the monolithic architecture of application development to microservices architecture.
What is Microservices Architecture?
Unlike monolithic architecture, where the entire application is developed as a single unit, Microservices structures applications as a collection of services.It enables the team to build and deliver large, complex applications within a short duration.
How can Microservices Work with DevOps?
Microservices architecture enables organizations to adopt a decentralized approach to building software. This allows the developers to break the software development process into small, independent pieces that can be managed easily. These developed pieces can communicate with each other and work seamlessly. The best part about microservices architecture is it allows you to trace bugs easily and debug them without leading to redeveloping the entire software. This is also great from the customer experience perspective as they can still use the software without any significant downtime or disruption. It’s a perfect fit for organizations that use DevOps to deploy software products.
No wonder organizations like Netflix, Amazon, and Twitter that were using a monolithic architecture have transitioned towards a microservices architecture.
Let’s look at the benefits of Combining DevOps with Microservice Architecture:-
Continuous Deployment: Remember the Netflix example we gave at the beginning about how Netflix reroutes the traffic to the old version of web images if they are not deployed on time? Imagine if Netflix still used monolithic architecture or the waterfall method of software deployment, do you think they would have been able to give the same kind of customer experience you witness today? Most likely, not! Microservices architecture coupled with DevOps enables continuous delivery and deployment of software, which means more software releases and better quality codes.
More innovations and More Motivation: Imagine working on a product for 2-3 years and then knowing it is not acceptable to the market!It becomes hard to pivot too. Often you realize that there are several bugs, the process has become unnecessarily lengthy, and you have no clue which team is working on what. Wouldn’t it lower your morale? However, those days have gone. Today, organizations have transitioned from a project to a product approach. There are smaller decentralized teams of 5-7 people that have their own set of KPIs and success metrics to achieve. This allows them to take ownership of “their” product and it gives them better clarity on the progress. It also gives them the freedom to innovate, which boosts their morale.
High-quality Products: With the power of continuous deployment and the freedom to experiment and innovate, organizations can continuously make incremental changes to the code leading to better quality products. It allows teams to mitigate risks by plugging the security loopholes, make changes to the product based on customer feedback, and reduce downtimes.
As you can see, using DevOps and microservices architecture together will not only boost the productivity of the team, but it will also enable them to develop a more innovative and better quality product at a faster pace. It helps product teams develop products in a granular manner rather than taking a “do it all at once” approach.
However, to embrace DevOps and microservices, you have to ensure that your teams understand the core benefits and make the most of the change.
Teams usually work in silos – the development team works independently, the testing team does its job, and so on. There is an obvious gap in communication, which leads to a delay in completing development and testing. DevOps and microservices require teams to work in tight collaboration. You will have to foster an environment where there are cross-functional teams of testers and developers communicating and working together to complete a task. This will help the teams to accelerate the process of developing, testing, and deploying their piece of work at a faster pace.
Of course, it is not easy to introduce a culture of collaboration, given that people are accustomed to working in silos. Hence, it is essential to reduce friction before starting the initiative. Once everyone shares in the vision and understands their own role in getting there, developing products with DevOps while leveraging a microservices architecture will become much easier.
Microservices is a method of structuring a software or an application as a loosely coupled service. Each service executes one function and is a component of the system that can be developed, maintained, and scaled individually. Each module supports one goal and uses a simple interface to communicate with other services.
Traditionally, software programs were built using a monolithic approach, where the entire software was built as a single unit. For eg., the entire unit would be responsible for database operations, executing business logic, handling HTTPS requests, background processing, client communication, user authentication, and so on.
In this structure, every tiny change in the system would require rebuilding, checking, and deploying the entire application. Change, even in a line of code, would prompt developers to make a new build of the complete system, which was time-consuming.
It became clear that the monolithic architecture was holding back innovation, scalability, agility, and independence. The microservices approach brings all this and more. Where there is a large system with intricate component management, scaling, and individual development, microservices has become the obvious choice.
However, making that move from monolithic to microservices isn’t easy. There are several considerations to factors in.
Key Considerations for Businesses Shifiting To Microservices Architecture :
Degree of Independence – The first thing to consider is the level of independence you want for the services in your microservices architecture. In the first approach, each service is completely independent with its own UI and database. This would be an instance of an extreme microservices architecture where services are entirely decoupled and share nothing. The difficulty arises in ensuring all datastores stay in sync and updated at all times. To rectify this, in the second approach, you can choose to share some components between the services, such as the database. This would make it easier for you to ensure data consistency and enforce data standards, regulations, and compliances within the software.
Technology Stack – It’s hard enough to decide a tech stack for a monolithic application. Now, imagine doing that for each service within a microservices architecture. If your services are heterogeneous, this could create an issue with standardization. Moreover, it becomes harder for your people to move between teams if every team uses a different tech stack. A recommended approach is to take a balanced approach in deciding the desirable tech stack across the application. If a team wants to override the default choice, they will have to support their decision with the pros and cons of the change that compelled their decision. The ideal tech stack may include the cloud provider, infrastructure, storage, monitoring, programming language, and a testing and logging framework.
Complexity – Microservices impacts the operational complexity of the underlying application. You need to consider aspects such as the infrastructure which needs to be scaled up and down for a sophisticated level of automation. Load balancing and scaling, which will happen either for all services or only a few, will be a concern. Service discovery as services in a microservices world changes dynamically due to upgrades and scaling and that should be part of the consideration set. As should be monitoring, which needs to be configured for each service individually. And, think of the capability to handle scenarios when a subset of services are scaled up or down.
Decouple Capabilities – There is a definite cost associated with moving to microservices from monolithic systems. Therefore, you’d want to consider what capability your systems have that can be decoupled and how you can migrate incrementally. Assess this in your system by taking a look at the operational readiness for creating services or migrating them. The primary idea is to start with capabilities that are fairly decoupled right in the monolith. These could be services that don’t require changes to several client-facing applications that use monolith and don’t use a data store. After decoupling simple edge services, consider those deeply embedded in the monolithic architecture.
Continuous Delivery – As Martin Fowler points out in his article on microservices tradeoffs, easily deploying small independent units is a blessing for development, but operations get more complicated as a few applications become hundreds of microservices. This reinforces the vital role of continuous delivery. While CD is a valuable skill for monoliths, it’s absolutely essential for microservices. Organizations such as Netflix and Amazon have spent their energy in building homegrown custom continuous delivery pipelines for microservices. As an alternative, organizations can choose a CD automation platform for a less intensive choice.
Data Services – Refactoring the underlying data structures is one of the most complex issues of migrating to microservices. There are several models that you can follow. Use reference data to populate drop-downs in GUIs, Master Data Management to eliminate several views of an entity such as the customer within a database, flat object structure to store documents such as feedback surveys, independent tables to support data retrieval with SQL, and blob storage for storing a structured Java object, for instance.
Team Organization – Lastly, you must reorganize your teams to ensure that all services are developed, deployed, and maintained independently. You may need an independent team to work on each microservice because when engineers work on multiple microservices, they might make optimization decisions that are not in the best interest of all associated services. Each team may need to have capabilities such as development, testing, Ops, database administration, UX, and product management. The central idea is to organize teams for maximum optimization of each microservice, without dependence on other teams.
Challenges of Shifting to Microservices and Why you need a Reliable Partner:
As you would have inferred, migrating from a monolithic architecture to microservices isn’t a job for everyone. When you need to make so many key considerations and weigh several pros and cons, you need specialized assistance from a reliable partner.
At ThinkSys, we make microservices possible even when you have a hyper-complex monolithic application architecture. We can ease the many hiccups you might encounter in that shift.
Back in 2011, Marc Andreesen said, “Software is eating the word”. These words have become even more relevant as digitization becomes an organizational priority for enterprises across sectors. The world is becoming software-driven and for the IT department, this has translated into increased demand for software to address ever-evolving requirements.
Users have high expectations of usability and demand greater flexibility in business operations. Frequent updates and upgrades are our new normal. Version 2.0 of a product is expected to be built almost simultaneously with the first version. Business applications need amendments and scalability according to the changing needs of business users. The task is huge. And the growing shortage of seasoned developers isn’t helping the situation.
Organizations are looking at ways to make software development faster, increase automation, and also make development easier to replicate, even by business users. They are looking at doing away with repetitive coding (something that weighs down traditional software development).
And this is how we came to know and love low-code and no-code software development.
The ABC of Low Code Software Development:
Low code software development is an accelerating trend. Research suggests the area growing from USD 4.32 Billion in 2017 to USD 27.23 Billion by 2022, at a CAGR of 44.49% during the forecast period. So, what is low-code development and what makes it so attractive?
Low code development is a methodology where manual processes are automated by employing a visual IDE environment and without hand-coding. The visual IDE environment connects to the backends and also to the application lifestyle management system. Low code provides avenues for developing custom code to deploy features that are not readily available.
Low code development is great for specific business processes especially those that need integrations with other applications, systems, and databases. Apart from giving developers the benefit of developing applications faster, low code development can also be done by the non-developers (mainly the power users) in the development team. These non-developers are usually not proficient developers but have some basic coding and scripting skills.
Low code development platforms such as Salesforce Lightning Platform, The FileMaker Platform, OutSystems, KissFlow, and Zoho Creator etc. allow developers to arrange application components including the application data and logic using a drag-and-drop interface. It is almost akin to building Lego models using virtual blocks- the developer moves the Lego block with a mouse and snaps it into their model.
But does this mean low code is right for every application? Well, not just yet. However, low code development is great for applications that are built for an express purpose as it not only accelerates the workflow but also focuses it.
What Advantages does Low-Code bring to the table?
If you are thinking of low-code development get ready to experience a slew of benefits.
Faster and more democratized application development including greater inputs from power-users
Reduced need for software developers in specific functions can lower costs
Greater agility for organizations to match market demands
Better risk management and application governance as low-code facilitates immediate change
Reduced complexity in app development that enables faster transformation in a digital world
The ABC of No-code Development:
Chris Wanstrath, CEO at GitHub says, “The future of coding is no coding at all”. But can this even be possible? Yes, it could with no-code development.
No-code development employs a visual development environment that helps anyone create applications. This platform uses a drag-and-drop method to add application components needed to create an application. The users need absolutely no coding knowledge. In fact,this could be anyone with an idea of building an application.
No-code development gives non-technical business users the ability to build full-fledged, complex applications that are powerful, secure, and, also, enterprise-grade. Surprised?
Such a platform uses a user interface builder, allows visual modeling to process and manage data easily and also allows for easy integrations. Essentially, a true no-code platform can be described as software that writes software. It has easy to use features such as drag and drop modules, picklist selection boxes, spreadsheet imports, etc. Tools such as Nintex, and Quick Base, and even options from Kissflow and Zoho Creator have been successfully giving users more freedom to develop applications with ease.
No code development is usually driven by specific use cases especially those that don’t need connections to third-party systems and are great for reporting analytics or tracking applications. In case such a connection is needed, it can be enabled quite easily as well.
So, What are the Benefits of No-Code?
Much like low-code, no-code development also gives us a host of benefits. It is hardly a surprise to see Forrester predicting that the no-code development market will grow from $3.8 billion in 2017 to $21.2 billion in 2022.
Greater agility as development happens visually using pre-built modules. Since the development time is reduced and testing is automated, organizations can build applications faster
Reduced overhead costs that come with having a team of highly skilled developers
Enabled re-usability as small parts of an application can be used in other applications
Enable change easily as all you need is new logic to implement change
Allows non-programmers to create applications at speed to fulfill business needs
Of course, the applicability is still limited and in truth platforms like low and no-code are just pushing the heavy lifting to platforms that do the hard work behind the scenes. But, that said, the value is clear.
No code and low code software development could revolutionize software development on getting established.Projects that used to take years and months can now be completed in days. The democratization of the development process also makes this methodology relevant for these times when collaboration has to be at speed to deploy new capabilities. It will be interesting to see how this plays out. Do you have a low-code or a no-code strategy in place?
The global AI (Artificial Intelligence) software market is set to explode. The numbers may increase from around 9.5 billion U.S. dollars in 2018 to a whopping 118.6 billion by 2025. In fact, many of the top names in the tech industry are investing in AI-related R and D in a bid to embrace futuristic solutions.
With AI penetrating almost every space possible, how can software development be left behind? Several firms are looking to accelerate software development and testing functions by using the combined power of AI and ML (Machine Learning).
Here are some of the ways in which Software Development is undergoing a Massive Transformation in the AI-age:
Coding Assistance – Clean code is the way to create stable software that is easy to maintain in the long-term. Both AI and ML make it possible to analyze the code and also, optimize it for better interpretation and performance. A case in point is that of AI-powered tools, which act as coding assistants. These tools come preloaded with learning culled from several thousands of coding rules and help developers fix their code. This cuts down the time for coding, brings to the table the most relevant coding instances, and helps developers. As an added benefit, developers can focus on more creative and intuitive tasks that drive innovation, rather than on repetitive or routine tasks.
Rapid Prototyping – More often than not, coming up with a software product needs tremendous amounts of planning before putting thoughts into action. AI and ML can cut short that duration by offering rapid prototyping capabilities. Think in terms of automated decision-making, optimized development process, early technical validation of ideas and so on. Ergo, developers can easily develop new applications in a shorter time, improve applications quickly and also, deliver enhanced customer experiences.
Bug Fixes – One of the key pillars of the process of software development is bug fixes. AI can completely transform this step. It can detect bugs using intelligent algorithms and without the need for any manual intervention. AI can identify high-risk areas of the code based on historical data or coding patterns. A focus on those areas can help find bugs faster. This also helps reduce the possibilities of bugs being overlooked or ignored by testers under time-pressure.
Software Testing – Like bug fixing, with AI at the forefront, the days of manual testing may be transformed before long. AI can help easily track the common errors and flag them early in the development. AI can also help analyze the system logs to flag down errors and, in the future, the same could be used to make dynamic changes without any human assistance. Then there is the potential impact on test automation. AI can analyze the massive volume of test cases and define which are the best cases for automation based on their priority and “automatability”. It’s only a short stretch from there to AI being able to assemble the scripts needed to automate those test cases on the fly. As the product evolves, AI can also determine how the test cases, and hence the scripts, need to be changed. In essence, AI can automate the creation of test automation!
Product Maintenance – From startups to corporates, a large part of the software development costs is spent on product maintenance. Oftentimes, even the redundant features of a software product are subjected to maintenance. This is costly and time-consuming. In the AI-age, it will be easy to identify such redundant features by scanning massive chunks of data. With intelligent automation, the process will be less complicated and less prone to errors as developers will not need to manually correlate the data from multiple sources. Any unrequired features and functionalities in the software and the associated code can be easily detected and removed if need be.
Bridging the Skills Gap – There’s a lot of hue and cry over AI taking over manual jobs and making certain roles redundant. But then, on the bright side, AI tools and technique can also help speed up the development process by making it easier for developers to develop products using more automated means. They won’t have to learn a new set of skills (at least not immediately). They could use ready components that can put together with little or no coding with AI doing much of the heavy lifting in the background.
For instance, consider Bayou. Bayou generates code snippets for accessing APIs in Java. All the users need to do is “Ask Bayou” for what kind of program would help them address specific tasks in their programming. Bayou then, analyzes the code from the user and the query and delivers the right API idiom. This innovative application of Neural Networks was jointly developed by Rice University and DARPA. Bayou, and others like it, represent the vanguard of the “AI coders” movement that could simplify simple software development tasks.
It seems abundantly clear that AI will become a fundamental business practice offering real business advantage to various industries, including software development. The way we develop software may be set to get more automated, less intuition-driven, and more AI-led.
What is your take on the AI-advantage for software development?
Does anyone even remember when companies developed an entire product, tested it, fixed it, and then shipped it? The entire process would take months, even years, before a functioning product made it to the customer. Before the product hit the market, neither did the potential customers know what it held for them and neither did the product owners know if it would hit or miss the mark.
Today, product users expect to be a part of the development process. They want to contribute their insights to develop a product that matches their ongoing needs. The need is for continuous innovation and improvements. The need is for DevOps!
DevOps combines technology and cultural philosophies to deliver products and services quickly. It is a continuous process of developing, testing, deploying, failing, and fixing applications to achieve market-fit. Jez Humble, one of the leading voices of DevOps sums it up “DevOps is not a goal, but a never-ending process of continual improvement.”
Today, DevOps is not just for a handful of large enterprises. According to Statista, the number of companies adopting DevOps went up by 17% in 2018.
A quick look at what has made DevOps popular?
Apart from the continuous innovations and improvements, DevOps also helps in:
Improving customer satisfaction: With a DevOps mindset, companies use advanced methods to identify issues and fix them real-time before the customer is impacted. There is also scope to improve the product on-the-go driven by frequent suggestions and feedback from customers. Continuous improvement in quality leads to customer delight. Take Rabobank of Netherlands, for example. This large financial institution has over 60,000 employees and hundreds of customer-facing applications. As the deployments were manual, the failure rate was over 20%, and they received many complaints about delays. When they moved to DevOps, they were able to deploy applications 30x more frequently with a lead time that was 8,000 times faster than their peers.
Change in organizational culture: DevOps has played a significant role in breaking silos and boosting the collaborative culture in companies. In an agile environment, working in silos can slow down the process of developing, testing, and releasing the product. A DevOps team will be able to collaborate better and ramp up the process of developing, testing, and troubleshooting the product.
A decrease in failure rates: According to the State of DevOps report, high-performing DevOps organizations have seen a reduction of failure rates of 3x, thanks to their ability to find and fix errors early in the cycle.
Higher productivity: DevOps organizations can deploy products 200x more frequently than a non-DevOps organization, leading to happier and highly motivated teams. Take Microsoft’s Bing, for example. It has moved developers to a DevOps environment with the idea of continuous delivery and innovation deeply ingrained within their processes. The result? Bing deploys thousands of services 20 times a week and pushes out 4000 individual changes every week. The continuous effort by the team to deliver has made Bing the second largest search engine in the world.
While adopting a DevOps culture is essential for a company to thrive, it is also crucial that they have the right architecture and systems in place to complement their principle of continuous delivery and innovation. That’s where microservices is now playing a massive role.
Micro-services and Their Role in DevOps Organization:
For a long time, companies relied on a monolithic architecture to build their application. As monolithic applications are built as a single unit, even a small change in a single element made it necessary to build a completely new version of the application.
With more and more companies moving towards DevOps, such a monolithic architecture makes it difficult to implement changes rapidly. The need for greater agility gave rise to a new type of architecture -enter microservices.
With Microservices, an application is built on small, independent components that are independently deployable. Although independent, these components communicate with each other via RESTful APIs. So, even if a single piece of code has to be changed in a single element, the developer does not have to build a new version of the whole product. They can simply make the changes to the individual components without affecting the entire application, making the deployment efficient and faster.
For companies that have adopted the DevOps culture, developing applications with microservices has several benefits that include:
Easy rectification of errors: When a component fails the test or requires changes, it is easy to isolate and fix. This makes it easier for companies to fix errors quickly without affecting the users of other services.
Better collaboration: Unlike a monolithic architecture where the different teams focus only on specific functions such as UX, UI, server, etc, a microservices architecture encourages a cross-functional way of working.
Decentralized governance: Monolithic architecture uses a centralized database, while microservices use a decentralized method of governance, wherein each service manages its database. This makes it easier for developers to produce tools that also can be used by others to solve specific issues.
A key trend accelerating the adoption of Microservices in such scenarios is Containerization. Containerization allows code for specific elements to be carved out, packaged with all the relevant dependencies, and then run on any infrastructure. These applications can be deployed faster and can be made secure. The applications are extremely portable and adaptable to run on different environments.
Companies like Amazon and Netflix have shifted to microservices to scale their business and improve customer satisfaction.
Product companies aiming to become customer-centric and delight with continuous improvement in the product may find it essential to adopt a DevOps mindset married to a transition to the microservices architecture.
Of course, it will take some time to transition product development. Teething problems are bound to arise, including duplication of efforts due to the distributed deployment system. However, given the larger picture and the potential benefits, it’s a wise move for product companies to make.
Google introduced AngularJS as an open-source framework specifically to address the challenges faced by the mobile app developer in creating and testing code. So, how does it do that?
AngularJS integrates HTML code along with the application modules.This forms an innovative framework that is now widely used to restructure JavaScript code. AngularJS is designed to help accumulate data by adding and eliminating extra source code to maintain the overall code integrity.
What is AngularJS?
In 2009, Google launched AngularJS, and it turned out to be a great framework for building iPhone and Android mobile apps. It doesn’t offer many add-ons but there are an array of features which makes it a perfect fit for the mobile app developer’s toolbox.
Its interactive abilities with backend, web servers and external data sources help mobile app developers build a feature-rich mobile app easily, quickly, and flawlessly.
A key value is that AngularJS helps to manipulate jQuery Document Object Model libraries. Another great feature of AngularJS is that it enables developers to expand the functionality of HTML by adding constructs through its two-way data binding. With its directives, mobile app developers can accumulate data into HMTL and can abstract DOM modifications.
A Summary of AngularJS:
AngularJS can be easily added to an HTML page through a simple coding script. The modular approach helps the mobile app developers segment codes, simplify testing, and update maintenance activities on different types of mobile apps. It has a few manipulations of JS and HTML, which adhere to the fundamentals, thus helping to deliver better stability.
It extends HTML templates, enabling an advanced approach in managing and editing the components. Mobile app development with AngularJS is extremely efficient, bug-free, and scalable.
Now that we are familiar with the basics of AngularJS; let’s explore how AngularJS can be very helpful in creating the best mobile apps with rich functionalities.
Build Mobile Apps with Advanced Development Practices:
Nowadays, every app is data-driven, and it makes obvious sense to use a tool that is built to address just this scenario. Simplifying the following three advanced software practices has made AngularJS the ideal choice here.
Three-way Data Binding:
Data binding has become a crucial aspect of mobile app development. When the data model updates, the UI (User Interface) must also change and update. And when app UIs updates, the mobile app developer updates the mobile to match with application’s UI. This is called two-way data binding. AngularJS takes that a step further.
Three-way data binding is an idea that the developers can not only keep the UI but can also sync the data with the backend services. This means the developers can take inputs from an app, process it, save it on the webserver, and keep the UI in sync. With jQuery iOS and Android Background, the developers find AngularJS’s three-way data binding a considerable time-saver for app development.
Reusable Codes:
It helps developers keep the UI, data, and code logic discrete. That means that AngularJS enables them to reuse older code on different devices and also on different platforms. It also allows the developers to customize their UI for each platform to provide a better user experience. In the past, application development was only limited to the PC and desktop.
But now, there are mobile devices with different operating systems, different functions, and different UIs. Thus, developers now have to create code for all the platforms separately. They cannot reuse that code. AngularJS addresses this issue by enabling the developers to reuse code which has been previously created.
Testability:
When it comes to end-to-end unit testing, AngularJS provide advanced support. It offers flexibility to the developers so that they can conduct testing more efficiently. It has impressive testing compatibilities like the dependency injection concept to reuse, maintain, and test the code. In dependency injection, the components are given specific dependencies. This helps to locate the code and makes it configurable. Thus, the quality analyst can quickly check the code and its dependencies and find bugs easily.
More Reasons to Use AngularJS:
Apart from three-way data binding, code reusability, and easy testing, there are other benefits that developers can experience while working with AngularJS.
Easy to understand: The code written in AngularJS is not only easy to understand but also easy to maintain and test.
Customization: While writing code, the mobile app developers do not require to add all the libraries at the same time while adding other modifications.
Wider Community: The Google community supports AngularJS which means the developers will get wider support to fix any issue.
Pre-defined Solutions: AngularJS comes with a pre-defined and versatile solution which can be used within the app. Developers can get pre-defined UI routing approaches and module practices to help them get their app created sooner.
Most Successful AngularJS Domains:
Businesses are looking to stand apart from the competition by creating impressive AngularJS mobile apps. Many tech-giants have significantly invested in AngularJS to provide a high-quality user interface and user experience. YouTube, Netflix, Upwork, and PayPal are some of the examples of mobile apps working on AngularJS. Developers looking to create mobile apps with great user experience are using AngularJS in domains like.
On-demand video streaming apps.
Travel and destination finder apps.
Weather updates apps.
User-generated and content portals.
Apps for user reviews.
Interactive social apps.
Clearly, AngularJS provides many benefits when it comes to creating seamless mobile apps. It is a futuristic framework which will continue to redefine the way mobile apps are developed. Does your mobile app strategy include AngularJS?
