As the programming ecosystem proliferates, a number of frameworks, libraries, and tools are being introduced to simplify the software development cycle. They are not just trimming the lines of code, but, are reducing the time from prototype to production.
While there are a plethora of options available, the pace of change is making many of these programming-aids obsolete, faster. However, there are a few that are here to stay and disrupt the software development system.
Here are a few programming tools, frameworks, and libraries that have defined their space in the programming world and have proven to be an inevitable part of it. They have evolved to ease developers’ life and are certainly going to change the way software development happens in the year 2018.
- The Node Package Manager (NPM) helps to manage modules in projects by downloading packages, resolving dependencies, and installing command-line utilities. Thanks to its ever-expanding community, NPM is the largest ecosystem of open-source libraries in the world.
For the incredible range of benefits that Nodejs comes integrated with, businesses are hiring Nodejs developers to raise the performance standards of software applications.
- With AngularJS, there is flexibility in development. Developers with expertise in HTML can use the language with new HTML syntax with new attributes (called directives) to extend the functionality of web pages.
- AngularJS is a MVC framework, where synchronization between the model and view happens with two-way data binding. The changes made in the model data are immediately reflected in the view (and vice versa). This automated and immediate updations ascertains that the components if the framework is updated all the times.
- The AngularJS directives can be used to create reusable components. A component enables hiding of complex DOM structure, CSS, and behaviour. That way, you can separately focus on how an application looks and how it works, separately.
Along with this, AngularJS brings in the benefit of client-side server validation, deep linking, DOM manipulation, etc.
4. .NET Core
.NET Core is an open-source, next-gen .NET framework by Microsoft. If an application needs to run on multiple OS platforms (Windows, Linux, MacOS), then .NET is a good fit for it.
.NET Core proves to be a compatible choice for server-based applications when there are cross-platform app requirements when there are high-performance and scalable systems, and involvement of Docker containers, microservices, etc.
Spring is an open-source application framework for developing Java enterprise applications. It offers an infrastructure that enables developing well-structured and easily-testable java applications, web applications, applets, etc.
- Spring is a dependency injection framework (Inversion of Control) that assigns dependencies to the object during runtime. When standalone programs start, the main program starts, create dependencies and executes appropriate methods. This makes the code loosely coupled and thus easy to maintain.
- Spring framework is built-in with templates for Hibernate, JPA, JDBC, JTA, etc., saving developers from writing too much code.
- Spring provides a consistent programming model, which is usable in any environment. There are web applications that don’t even need high-end servers and can be run on a web container like Jetty or Tomcat. Also, not all applications are server-side applications. Spring provides application models that insulate application code from environment details like JNDI, making code less dependent on its run time context.
Django is an open-source framework for web application development, written in Java. It follows the model-view-template (MVT) architectural pattern and is a fit for complex, database-driven applications.
Django, launched in 2005 is a part of well-known websites today, including Instagram, Nextdoor, BitBucket, Disqus, Pinterest, and more. The Python-based framework supports reusability, rapid development, less code, and low coupling. The main Django distribution includes a number of applications, which simplifies development to an extent. This includes an extensible authentication system, built-in mitigation for web attacks (like SQL injection, cross-site scripting, password cracking, etc.).
Tensorflow is a machine learning framework by Google, meant for creating Deep Learning models. Deep Learning, a subclass of ML deals with Artificial Neural Networks (ANN) that makes a system learn and progressively improve with experiences. Tensorflow is based on the computational graph, having a network of nodes. Each node is an operation, running some function, which could a simple mathematical calculation or complex multivariate analytics.
While a number of brands are putting-in their trust in this ML framework (like Dropbox, Twitter, Uber, Intel, etc.), Google itself utilizes the power of TensorFlow in many of its services. This includes Google Recognition, Google Search, Google Photos, etc. This mature framework is a part of small and large scale AI development projects.
“According to Stack Overflow survey results 2018, machine learning is one of the important trends in the software development industry. Languages and frameworks associated with ML are on rising, and developers working in these areas are high in demand.”
Cross-platform native apps are the future of mobile app development, and Xamarin is one of them. Xamarin offers an edge over the proprietary and hybrid development models as it allows developing full-fledged mobile apps using a single language, i.e. C#. Moreover, Xamarin offers a class library and runtime environment, which is similar to the rest of the development platforms (iPhone, Android, and Windows).
- Xamarin offers a less complex environment for development, as compared to other native cross frameworks. When it’s about code sharing, cost-saving, and ease at maintenance, Xamarin for cross-platform native development proves to be a better option over hybrid apps. Less memory utilization, faster loading of datasets, less CPU time utilization are some of the benefits that Xamarin offers over hybrid app development.
- As compared to other cross-platform native development platforms in the market, Xamarin has the most stable and updated SDK. Also, Xamarin integrates well with Azure, which gives the benefit of developing advanced and secure cloud backend for the apps.
Spark is an open-source, micro framework, meant for creating web applications in Kotlin and Java. Spark was open-sourced in the year 2011 and its new version Spark 2.0 was launched for use in the year 2014, which was primarily centered on the Java 8 lambda philosophy.
The Java Virtual Machine (JVM), one of the biggest programming ecosystems has got a number of java web frameworks. However, java web development has always been cumbersome. For those who love JVM but don’t want the frameworks or verbose code, Spark is the solution.
There is a long list of tools, frameworks, and cloud services that are available to augment the performance of Cordova. Some of the popular names include Visual Studio, Ionic, Framework7, Monaca, Mobiscroll, etc. Considering the potential that Cordova brings in, the contributors to this framework are some of the tech-giants, including Adobe, Microsoft, Blackberry, IBM, Intel, etc.
Hadoop is an open-source framework by Apache that stores and distributes large data sets across several servers, operating parallelly. One of the major benefits of Hadoop over traditional RDBMS is its cost-effective system for storing giant data sets.
The core of Apache Hadoop is Hadoop Distributed File System (the storage part) and Mapreduce Programming model (the processing part). Hadoop is written in Java, the widely used language by developers, which makes it easy for developers to handle tasks and process data efficiently. Hadoop’s MapReduce enables processing terabytes of data in minutes; it’s that fast!
12. Torch/ PyTorch
PyTorch is a machine learning library for Python. PyTorch is primarily created to overcome the challenges of its predecessor, Torch. Owing to the unwillingness of amongst developers to learn the language Lua, Torch was unable to experience the success that Tensorflow did, in spite of being into the mainstay for computer vision for years. It enables writing new neural layers in Python by using libraries and packages like Cython an Numba.