Credits : Forbes

 

As a Software Engineering Manager, how can I continue growing my technical skills?

A lot of the techniques I recommend may apply equally well for engineers who aren’t making the switch to management, but I do think the risk is higher for managers and so it requires more deliberate investment. Engineers who are spending most of their time writing code are learning by doing, and are constantly reminded of how important it is to learn new things. However, managers may spend most of their time on non-technical issues, and so it’s easy to under-invest in technical knowledge.

The first step is to convince yourself that technical knowledge is important to your career as a manager. Firstly, while I do think that great managers can still support a team well if they are not technical experts in that area, all else equal, more technical knowledge will make you a better manager. For starters, it will be easier for your team to trust you and find you credible. It will also enable you to ensure your team is making the right technical decisions. Next, it will make it easier for you to coach your team, assess how they’re doing, and teach them to grow their own technical skills. Finally, especially in a field like software, there’s a risk that the industry will “drift” and your knowledge will become stale. So even if you’re in good shape now, it will still require constant investment to maintain your level of understanding.

Next, you need to find techniques that work for you. I find a mix of the following to be helpful:

Reading: I probably spend 30-60 minutes per day reading.

Blogs and articles:
I use Feedly primarily for this, and whenever I find anything online (on social media, etc.) that hasn’t made it into my Feedly, I add the source to Feedly. I also basically have a habit that whenever I have some downtime I try to open up my Feedly instead of social media. Hundreds of articles make it into my feeds, and though I only read a small subset of them, it’s still valuable to skim the headlines to see what people are writing about. So this also works a bit like a “discovery” mechanism to help me identify things I can invest more in learning using some of the techniques below. Basically, if I find something that seems like it’s important to my job or to engineers on my team, but I feel like I don’t understand it and can’t coherently explain it, it’s something I’ll find a way to learn more about.
More recently, I’ve tried to find curated or community-based newsletters. To name some of my favorites, I use DiscoverDev, O’Reilly’sFour Short Links, and HNDigest (which is just a daily aggregation of the top threads on HackerNews, that way I don’t have to remember to visit it myself). Sites that have discussion threads attached to links are valuable for supplemental content and to help me gauge the credibility of an article if it’s on a subject I know less about. If you have other recommendations, leave them in the comments and I can add them!
A lot of people I look up to recommend investing in a good Twitter account, but again, I’d rather not have the upkeep of building out my follows (I don’t use Twitter that often). There are some hacks, like using Nuzzel to see someone you look up to’s Twitter feed.
Books:
There’s always a bit of a lag between what’s happening in industry and what makes it into books, and they’re also a larger investment (of time and money), so I use them mostly for meaty, more evergreen content. Things like writing clean code, technical architecture, systems design, etc. are great book material. Sometimes, books are also a great way for me to pick up a new technology (I skim the book, then can use it for reference later), but if the technology itself is really new, technical documentation might be a better way to learn.
I also read a lot of non-technical books (and to force myself to understand and remember then, I try to write summaries on Quora).
Other:
Quora is also a great place to find content that is a little more authentic and less polished than books or even blog posts. You might think I’m biased since I work here now, but I’ve always found it a valuable resource, even before I joined the company (in fact, it’s one of the main reasons I was so excited to join).
Talking to people:
I try to stay in touch with smart former classmates and colleagues. The primary reason is mostly because I value the relationships directly, but as a side effect, it’s an awesome network to tap into for learning things. Whenever we catch up, I try to ask them questions about how their current company does things, or what they’re learning and if they have any advice for things I can learn.
Another great channel for learning new things is interviews. As a manager, you’re probably interviewing other people from different companies all the time. I use this opportunity to ask people what sort of things they’re learning, how they’ve solved certain technical problems in previous roles, and how they might solve certain technical problems I or my team are facing at the moment. It’s actually a great way to both learn things, and get signal on what sorts of things a candidate finds exciting and how they solve problems. I’m also “meta-learning” as I do this. For instance, if they tell me they used a particular technology to solve a problem, I asked them how they learned about that technology in the first place. You should, of course, make sure you don’t do this in an exploitative way (for instance, don’t ask a candidate to divulge anything that might be sensitive or confidential).
Trying/doing:
I find it really valuable to carve out time to just write code and try new things. As a manager, I don’t have as much time to do this at work (and also, I don’t want to necessarily be limited by my company’s current projects), so I often do this outside of work. For instance, things that I’ve explored within the past few months include: Amazon’s SageMaker, Terraform, Apache Spark, Apache Kafka, and Golang. Sometimes it’s just getting the tool set up and playing around with it, doing some tutorials, or building a fun side project. Trying things out helps go beyond.
I actually have a whiteboard at home next to my desk where I maintain a list of things I’d like to try out if I had more time. That way, when I do get that random downtime, I use that list. Random downtimes in my past have come in the form of everything from cancelled plans on a weekend, to being stuck at home due to really bad weather, to being under curfew during the Egyptian revolution.
Other:
Meetups: I’d like to do more of these, it’s just logistically more difficult than some of my other techniques, and I haven’t cracked the best way to make the most of the time I spend on them.
Conferences: Even though I don’t attend them in person all the time, many conferences will upload videos or summaries of talks given there, and those tend to be really valuable as well.
Find something that works for you. Different people learn in different ways, so if you find it enjoyable, and can incorporate it into your normal work/life time, you’re a lot more likely to stick with it.

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Credits : Theserverside

 

Whether you’re a seasoned Java programmer or a Java neophyte, testing Java applications is important. It ensures your application does what it’s supposed to.

Fortunately, there are plenty of automated testing tools for Java. In fact, there are so many it can be difficult to know which tools to adopt and use. To help solve that dilemma, we’ll discuss some of the most useful automated testing tools for testing various aspects of Java applications.

Types of Java testing tools

It’s important to note not all testing tools are designed to serve the same purpose. There are multiple types of testing associated with Java applications:

  • Unit testing helps you test newly written code before it is integrated into your codebase.
  • Integration testing ensures that new code will not break other parts of the application.
  • Performance and user experience testing verify your application is sufficiently responsive, stable and user-friendly.
  • Security testing helps identify security vulnerabilities in Java code.

(This list is admittedly not completely inclusive. We haven’t mentioned system testing and acceptance testing, for instance, although these overlap somewhat with performance and user experience testing.)

Some automated testing tools for Java support only one type of testing, but many can actually support multiple categories of test automation.

Best automated testing tools for Java

GitHub hosts nearly 2,000 repositories with the term “java testing” in their name. Not all of those correspond to distinct tools, but it gives us an indication of just how many automated Java testing tools are out there.

So which tools should you learn? Here’s a quick guide through the valuable automated testing tools for Java applications.

JUnit

As its name implies, JUnit is a Java testing tool designed for unit testing. It’s probably the most widely used unit testing tool for Java applications.

On its own, JUnit is not useful for much beyond unit testing. However, you can use it in conjunction with other Java testing frameworks to help automate other types of testing, like integration and acceptance tests. You can even use it in conjunction with Selenium.

TestNG

TestNG is a general-purpose Java testing tool that supports a range of test categories: unit tests, functional testing, integration tests and more. As such, TestNG is a versatile tool to have in your Java testing arsenal.

JTest

JTest is a Java testing tool that has been around for more than twenty years. Like TestNG, it is designed to support a broad set of test types. It’s another useful all-around Java testing tool to have on hand.

What makes JTest particularly interesting, however, are its static analysis features. You can use them to detect performance problems and security vulnerabilities within Java applications. Few other general-purpose Java testing tools extend into these testing categories.

The Grinder

The Grinder is a load testing and performance testing framework for Java. It helps to ensure that Java applications can handle the traffic that deployment teams intend to throw at them. You write your test scripts in Python — specifically, the Jython implementation — which makes Grinder an ideal Java testing tool for performance testing.

Gatling

Gatling is another Java performance testing tool. It uses a domainspecific language (DSL) for test scripts, which is one major difference between Gatling and The Grinder. Gatling also provides detailed performance testing summary reports. If you’re someone who likes having analytics data summarized in graphs and charts, you’ll like Gatling.

Selenium

Selenium is the most widely used tool for interface and user experience testing. It supports tests on virtually any of the major web browsers, as well as Linux, macOS and Windows.

Selenium’s major limitation is that it is not designed for testing mobile applications. For that task, you’ll want a tool like Appium or Selendroid, which are based on Selenium.

Mockito

To test modern Java applications efficiently, you often need to take advantage of mocking. Mocking lets you simulate external resources to see how they interact with your application. You can therefore test your application as if it were running in a real-world environment, but without having to set up an actual environment.

Mockito is one of the leading mocking frameworks for Java testing. Used in conjunction with a unit testing tool like JUnit, Mockito makes it fast and easy to write automated Java tests.

PowerMock

If you want to take advantage of mocking but find Mockito overly complicated or limited in functionality, PowerMock can help. PowerMock is a unit testing framework for Java that extends the functionality of Mockito — as well as EasyMock, another mocking tool. It lets you do things that Mockito can’t, like mocking of static methods.

Arquillian

Arquillian is a somewhat lesser known, but quite interesting, automated testing tool for Java. Arquillian’s goal is to allow developers to test on a large scale without mocking.

Instead, Arquillian lets you write tests that execute in real runtime environments. The idea is to provide the flexibility and efficiency associated with mocking, while delivering the accuracy of tests performed in real environments.This approach can seem a bit unorthodox to old-school Java developers, but it can prove valuable.

***

The list above is by no means exhaustive. There are many other automated testing tools for Java out there. And the best Java testing tool for you will depend on your needs and preferences, of course. But it always helps to familiarize yourself with some of the handiest tools for various kinds of testing with Java applications.

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Credits : Arnnet

 

NetSuite partner JCurve Solutions (ASX:JCS) has announced the acquisition of the business and assets of Sydney-based Riyo Business for $600,000.

Riyo Business’ software, Riyo, is a platform to provide on-demand or scheduled booking, dispatch and payment (BDP) services to businesses.

JCurve told shareholders on 14 May that the Riyo platform has been successfully piloted with two enterprise customers in Australia.

“The acquisition of the Riyo software platform adds to our growing suite of solutions,” JCurve Solutions CEO Stephen Canning said.

“We are purchasing a robust, scalable, industry standard technical architecture which has been successfully tested through product trials. The software platform could be rolled out in its current state or may be developed into other verticals and integrated into our ERP offerings,” Canning added.

Canning also told shareholders that JCurve expects the acquisition to be Earnings Per Share “EPS” accretive in the short term, to deliver long term shareholder value. He also said that the acquisition is in line with JCurve’s diversification strategy.

“The Riyo software platform is a disruptive technology which has multiple potential commercial applications,” Riyo Founder and CTO Adam Kundrat explained.

“Customers demand convenience, transparency and engagement in a single, mobile experience. The Riyo software platform leverages advanced technology to deliver this experience.”

JCurve posted a net profit before tax loss of $161,864 for the six months ending 31 December 2017. Overall, the Sydney-based cloud technology company closed the half year with net profit after tax (NPAT) of $393,427.

The revenue for the period increased by 11 per cent, to $5 million, as a result of organic growth, the company told shareholders on 21 February.

 

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creduts : Jaxenter

 

Technical expert Infomentum has partnered with IT education organisation EDITx to host the UK’s first Java challenge, launching on April 9, 2018.

The challenge invites Java students and professionals alike to enter an online gamified competition. They compete in separate leagues, alongside their peers, so that everyone gets a fair chance to be crowned UK Java Knight.

Both renowned Java experts and University Professors accepted to play the game as jury members and to write the questions of the challenge.

The jury members are :

  • Helene de Ribaupierre: Lecturer at Cardiff university
  • Oliver Szymanski: Founder of the Association of Java User Groups, Speaker & Director at Source Knights Ltd.
  • Mani Sarkar: Software Craftsman, Java/JVM Developer, Conference speaker, Blogger, various dev. comm. & conf, infra/containers/virtual tech & Steering Committee Member at DevoxxUK
  • Mauricio Salatino: Author, International Speaker & Principal Software Engineer at Alfresco
  • Siva Narayan: CTO, Author, Key Note Speaker – “Data Asset” “The Next Data Rush” at Focus Group Ltd.
  • Maurice Naftalin: Java Champion, Author, Speaker, Associate at JavaSpecialists.EU & Technical Director at Morningside Light Ltd.
  • Carlos Matos: Senior Lecturer at Royal Holloway University of London
  • Sandro Mancuso: Author, International Speaker, Founder of the London Software Craftsmanship Community, Software Craftsman & Managing Director at Codurance
  • Peter Lewis: Lecturer in Computer Science at Aston University
  • Peter Lawrey: Java Champion, Blogger, Speaker & CEO at Higher Frequency Trading Ltd.
  • Michael Kölling: Vice Dean for Education, Faculty of Natural and Mathematical Sciences at King’s College London
  • Jens Krinke: Senior Lecturer at University College London
  • Jeroen Keppens: Lecturer at King’s College London
  • Dan Hardiker: Chief Technical Officer at Adaptavist.com Ltd.
  • Antonio Filieri: Lecturer (Assistant Professor) at Imperial College London
  • William Clocksin: Dean of the School of Computer Science at University of Hertfordshire
  • Jaap Boender: Lecturer at Middlesex University London
  • Rabih Bashroush: Reader at University of East London
  • Ruben Badaró: Executive Director at Morgan Stanley
  • Shaha Alam: Oracle Fusion Middleware Consultant at Infomentum

The rules are easy: The participants have 15 minutes to give the correct answer to as many Java questions as they can (maximum 17 questions). The questions are multiple-choice with one or several good answers and an associated score 1, 2 or 3 points if answered correctly. A wrong answer results in a score of (-1) irrespective the level of the question.

The Challenge allows the usage of three “jokers”: the “50/50”, “Ask the challengers”, and “Ask a friend”. The total score will be the cumulative score of all questions answered within the set time frame of 15 minutes.

After 2 weeks, hundreds of Java profiles located all over the United Kingdom registered and participated in the challenge. The challenge is still open for 3 more weeks (ending May 25, 2018).

Last week, the companies Cisco and Dimension Data were organizing a Python challenge. A 16 years old boy surprised everyone by ranking second at the finals in the Cisco Head Office in Brussels.

The current leader in the IT Student category comes from the University of Leeds; his score is 58. The current best professional is working at Amazon Development Center; his score is 68.

The youngest participant is currently 18 years old and the oldest is 73 years old.

Three main purposes for contestants

(a) Fun, emotion and a bit of stress; a great competition with prizes to win.

(b) It’s an opportunity to learn some things: participants can refresh or improve their skills, benchmark themselves and compare to others, learn more about Java or just discover it.

(c) It’s a place suited for social interactions between contestants; they can follow Java leaders and influencers (the jury members!)

What’s in it for you

  • First Prize Student & Professional: £800 flight voucher for use on your choice of over 300 airlines
  • Second Prize Student & Professional: £500 voucher to be used for an experience of your choice. You could: have a romantic getaway, experience a supercar driving track day, spend a weekend glamping in a treehouse or more!
  • Third Prize Student & Professional: £250 Amazon gift voucher

Infomentum, the sponsor of the challenge may contact the participants:

  • To share challenge results
  • To award the prizes to the best participants
  • To share future intern or job opportunities

Tips 

“Swot up and think laterally. The answer you seek is right in front of you.” – Dan Hardiker, Chief Technical Officer at Adaptavist.com Ltd.

“My main advice for the contestants will be to pay a lot of attention to detail.” – Carlos Matos, Senior Lecturer at Royal Holloway University of London

“Use the jokers at the right time and keep a close eye on the clock.” – Alexandre Dembour, Partner EDITx

 

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Credits : Eurekalert

 

The Project Jupyter team has been honored with an Association of Computing Machinery (ACM) Software System Award for developing a tool that has had a lasting influence on computing. Project Jupyter evolved from IPython, an effort pioneered by Fernando Pérez, an assistant professor of statistics at UC Berkeley and staff scientist in the Usable Software Systems Group in Lawrence Berkeley National Laboratory’s (Berkeley Lab’s) Computational Research Division.

The award and a prize of $35,000 will be presented to the team at the ACM Awards banquet in San Francisco on June 23, 2018.

Project Jupyter is an open, international collaboration that develops tools for interactive computing: a process of human computer interplay for scientific exploration and data analysis. The collaboration develops applications such as the widely popular Jupyter Notebook, an open-source web app that allows users to create and share documents that contain live code, equations, visualizations and narrative text.

Today, more than 2 million Jupyter Notebooks are hosted on the popular GitHub service, covering technical documentation to course materials, books and academic publication. Jupyter has been transformative in scientific collaborations and reproducibility, as exemplified by its use at the LIGO observatory, whose discovery of gravitational waves was recognized with the 2017 Nobel Prize in Physics. The LIGO Open Science Center publishes Jupyter Notebooks that allow anyone to replicate their original analyses. Jupyter Notebooks also serves as a core infrastructure for research endeavors like the Department of Energy (DOE)-funded KBase platform for predictive biology, the GenePattern Notebook project from the Broad Institute and UC San Diego and the European Union-funded OpenDreamKit project that is building virtual research environments for mathematics.

JupyterHub supports the deployment of Jupyter tools in multiuser environments, from small research groups to universities, companies and other organizations. JupyterHub is used in numerous commercial companies, research at facilities such as CERN and high-performance computing centers like DOE’s National Energy Research Scientific Computing Center (NERSC) and San Diego Supercomputer Center (SDSC).

“The flexibility of the Jupyter architecture makes it easy to deploy in a variety of scenarios: while individual users can run the tools on a personal laptop or workstation, the same tools can be deployed on remote resources,” says Shane Canon, a project engineer at NERSC. “In fact, NERSC offers Jupyter as an interactive tool for remote access to its supercomputing resources.”

At UC Berkeley two new courses Foundations of Data Science and Principles and Techniques of Data Science, will be supported by Jupyter Notebooks deployed in the cloud and integrated with campus authentication. The courses are being offered as part of UC Berkeley’ new data science major. Pérez will be teaching the upper-division course Principles and Techniques of Data Science.

In industry, the Jupyter Notebook is widely used as a daily computation and data-analysis tool, and major companies have created hosted services based on Jupyter. Google’s Cloud DataLab, Microsoft’s Notebooks on Azure and IBM’s Data Science Experience all offer Jupyter Notebooks on their respective cloud infrastructure.

In education, at least 45 different courses use Jupyter Notebooks to teach a wide variety of subjects, including high-school level Computer Science, Aerodynamics, Numerical Methods, Statistics, Computational Physics, Cognitive Science and Data Science. These have been deployed at leading universities in the U.S. and abroad, including UC Berkeley, Cal Poly, MIT, Harvard, Columbia and Imperial College.

As a graduate student studying physics at the University of Colorado in the early 2000s, Pérez remembers using a hodgepodge of software systems to illustrate code, equations, visualizations and text in his scientific computing papers. This inspired him to create a unified environment for scientific computing. He found researchers around the globe that had all independently started building scientific computing tools in Python and combined these disparate efforts into one open-source platform called IPython–“I” for interactive. The program was free, and anyone could inspect its code, modify it and make the output available under liberal licensing terms.

Over the years, IPython evolved to meet the needs of various communities and in 2014 project rebranded itself as “Jupyter” to recognize the fact that it was no longer just for Python. In 2015, Pérez and Brian Granger of California Polytechnic University, San Luis Obispo received $6 million from the Leona M. and Harry B. Helmsley Charitable Trust, Alfred P. Sloan Foundation and Gordon and Betty Moore Foundation to expand and improve the capabilities of the Jupyter Notebook.

Since then, Pérez and Granger have secured additional funding from other sources like the DOE and industry partners like Google, Microsoft and Anaconda Inc. Companies such as Bloomberg, IBM, Microsoft, Netflix, Rackspace and Anaconda also support the project, either with services or with the time of engineers who actively contribute to Jupyter’s development. The next-generation user interface for the Jupyter Notebook, known as JupyterLab, is currently being developed in an open collaboration with team members and engineers from Bloomberg and Anaconda.

“One afternoon in late 2001, I was a physics graduate student at the University of Colorado working on my dissertation and decided to spend an afternoon writing the original, tiny version of IPython,” says Pérez. “I could not have imagined that this would grow into a worldwide platform almost two decades later. For me, it’s been a wild ride, made possible by going from a personal exploration to an open collaboration with an incredible team ”

“This is a project that has demonstrated 20 years of intellectual contributions with major impact in research, education and industry, and it continues to make its advances available to the world as an open platform,” says Kathy Yelick, Associate Laboratory Director of Berkeley Lab Computing Sciences. “The ACM System Software Award is an incredible honor, and this team is entirely deserving of this recognition.”

###

In addition to Pérez, other members of the Jupyter Project collaboration include Brian E. Granger and Carol Willing (Cal Poly San Luis Obispo), Matthias Bussonnier (UC Berkeley BIDS), Paul Ivanov and Jason Grout (Bloomberg), Thomas Kluyver (European XFEL), Damián Avila (Anaconda, Inc.), Steven Silvester (JP Morgan Chase), Jonathan Frederic (Google), Kyle Kelley (Netflix), Jessica Hamrick (DeepMind), Sylvain Corlay (QuantStack), Peter Parente (Valassis Digital).

NERSC is a DOE Office of Science user facility.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Credits : Laravel-news

 

Updates to the Heredoc and Nowdoc syntaxes proposed in a php.net RFC have been made for the upcoming PHP 7.3 release. The updates focus on improving look and readability:

The heredoc and nowdoc syntaxes have very rigid requirements. This has caused them to be, in-part, eschewed by developers because their usage in code can look ugly and harm readability. This proposal therefore puts forth two changes to the current heredoc and nowdoc syntaxes:

  1. To enable for the closing marker to be indented, and
  2. To remove the new line requirement after the closing marker

The current implementation as of PHP 7.2, might look like this simple example:

<?php
class foo {
    public $bar = <<<EOT
bar
EOT;
}

In 7.3, the following is valid:

<?php
class foo {
    public $bar = <<<EOT
    bar
    EOT;
}

The indentation of the closing marker determines how much whitespace gets stripped from each new line within the heredoc/nowdoc:

<?php

// 4 spaces of indentation
echo <<<END
      a
     b
    c
    END;
/*
  a
 b
c
*/

In the current implementation of PHP 7.2, a new line must be present to terminate a heredoc/nowdoc. PHP 7.3 removes this requirement:

<?php

stringManipulator(<<<END
   a
  b
 c
END);

$values = [<<<END
a
b
c
END, 'd e f'];

Background on Heredoc and Nowdoc

Nowdoc is available in PHP as of v5.3.0 and differs from Heredoc in the same way that a double-quoted string differs from a single quoted string. No parsing is done inside a Nowdoc, which has added single quotes around the opening marker:

<?php

$name = 'Example';
$str = <<<'EOD'
Example of string $name
spanning multiple lines
using nowdoc syntax.
EOD;

The above nowdoc output will be the literal string:

Example of string $name
spanning multiple lines
using nowdoc syntax.

A Here Document is defined as follows:

In computing, a here document (here-document, here-text, heredoc, hereis, here-string or here-script) is a file literal or input stream literal: it is a section of a source code file that is treated as if it were a separate file. The term is also used for a form of multiline string literals that use similar syntax, preserving line breaks and other whitespace (including indentation) in the text.

The addition love given to Heredocs and Nowdocs should make using them in PHP more readable and less error-prone due to indentation and end in a newline. Also, the output formatting will be much cleaner because of stripping the indentation based on the closing marker.

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Credits : Dabcc

 

This blog post was co-authored by James Ashley, MR and AI Architect, Microsoft MVP.

Developers sometimes get anxious when it comes to hooking up a database for their apps. However, with Azure Database for MySQL and Azure Database for PostgreSQL, quickly propping up and accessing a relational database is a piece of cake. These lightweight, open source database services provide a great way to get small apps and prototypes started with very little effort. Without any extra work on your part, you can automatically take advantage of built-in security, fault tolerance, and data protection. You also can use point-in-time restore to recover a server to an earlier state—as far back as 35 days.

Azure Database for MySQL and Azure Database for PostgreSQL will work with whatever kind of project you are creating, whether it is a Linux app running in a Docker container orchestrated by Kubernetes, a computer vision service using Python, or a simple ASP.NET website to display travel photos.

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Credits : Technotification

 

No surprise, again Python is the best programming language. Let’s take a moment to appreciate Guido Van Rossum and Python Community for keeping it simple and top-notch coding language.

Competition and comparison among the programming languages are not new. It is an old school concept following from the academics and interviews. But the trend on social media platforms became the battlegrounds for such rivalries.

In such battleground, Python has again become the best programming language. Dominating with the highest 31% of votes comparing to other languages that are nominated by Linux Journal readers via Twitter. Following this C and C++ languages stood at the 2nd and 3rd positions with 20% and 14% of votes.

Why Python?
There are many reasons for Python to win the best programming language by Linux Journal Readers Choice award. However, the developers perspective and the features stand the best out of those reasons.

Developers are optimistic about everything while choosing language or writing code. They strive for a smart, effective, and efficient solution for a problem. That is the reason for them to vote for the simple, fast, and robust programming languages to write snippets.

As a matter of fact, Python has all those super features that simplify the task of programmers. It is an extremely high-level language that is simple to write and understand. Python is an open-source language that is constantly innovating with different communities around the globe. It is already on top of 5 Best Programming Languages for Artificial Intelligence Systems.

Whereas, applications of Python are numerous in different fields. And it is the language almost compatible with many code editors. Python is also recognized as an official programming language for Google. Thereafter, it has the flexibility to adapt and extend to the new technologies like AI, machine learning, IoT, and Blockchain.

Become a Python programmer
Are you ready to learn the best programming language? There are many resources and documentation available for users to become the efficient Python Programmer. You can check our article: What applications do you develop with Python Programming? to know more about its applications. You can also read this article: Which one is preferable for Machine Learning? R or Python! if you are interested in developing machine learning applications. Following are the useful resources to learn python from beginner to expert:

1. Google’s Python Class

2. Python Documentation

3. LearnPython

4. EDX Python Courses

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Credits : Zdnet

 

Stack Overflow has released the results of its annual survey of 100,000 developers, revealing the most-popular, top-earning, and preferred programming languages.

The most-loved languages are Kotlin and Mozilla-developed Rust, according to Stack Overflow’s 2018 developer survey.

TechRepublic: 100K developers share most loved and most hated programming languages

This was the first time developer community and jobs site Stack Overflow asked coders about the Java-friendly Kotlin, probably because Google gave it full support in the Android Studio integrated development environment last year.

Developer analyst firm RedMonk this week noted Kotlin is the second-fastest growing language after Apple’s Swift for iOS and macOS app development.

Other preferred languages include Python, Microsoft’s TypeScript, Google’s Go, Swift, JavaScript, C#, F#, and Clojure.

Among the most-loathed languages are Visual Basic 6, Cobol, CoffeeScript, VB.NET, and VBA.

However, JavaScript takes top spot as the most-popular programming, scripting, and markup language. It is followed by HTML, CSS, SQL, Java, Bash/Shell, Python, C#, and PHP. Python is one of the fastest-growing languages, according to Stack Overflow.

Microsoft’s Visual Studio Code is the overall most-popular development environment, while the most-loved platform is Linux. Google’s TensorFlow is also a popular tool.

The survey asked developers about their attitudes to the future of artificial intelligence and finds 73 percent are more excited about its possibilities than concerned by its dangers.

Only a quarter of respondents agree with Tesla CEO Elon Musk’s view that government should regulate AI. Musk this week said the cutting edge AI he sees “scares the hell out of me”.

Developers in the US working with Erlan, Scala, Ocaml, Clojure, Go, Groovy, and Objective-C are the highest paid, earning salaries of $110,000 to $115,000. Developers working with F# have the highest salaries worldwide.

The median salary of developers worldwide is $55,000, but the median salary of developers in the US is nearly double at $100,000. Other countries with median salaries above $90,000 include Switzerland and Israel, while programmers in the UK and Germany earn on average just over $60,000.

Stack Overflow took on board criticism of how last year’s survey approached women and other minorities who use the site. This report doesn’t include last year’s chapter on the ‘Female Developer Age’ or questions such as whether developers prefer Star Wars or Star Trek, or identify as ninjas, rockstars, or gurus.

Over 92 percent of this year’s respondents are men but Stack Overflow says nine percent of US respondents to the 2018 survey are women. Worldwide just 6.8 percent of respondents are women, marking a one percent drop on last year’s survey.
“We had survey participation at almost the rate we would expect from our traffic. In regions including the US, India, and the UK, women are represented at higher levels among students than among professional developers,” Stack Overflow says.

The survey also looked at the representation of women and men in different developer roles.

Stack Overflow finds that women have the highest representation as academics, QA developers, data scientists, and designers. System admins and DevOps specialists are 25 to 30 times more likely to be men than women.

Microsoft languages seem to be hitting the right note with coders across ops, data science, and app development.

Which programming language do coders hate most?

Developers cast their votes on most and least favourite languages.

These five programming languages have flaws that expose apps to attack

Securely-developed apps may be at risk due to security issues in popular interpreted programming languages.

7 programming languages that every developer should learn in 2018 (TechRepublic)

Certain coding languages are more likely to net you a developer job than others.

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Credits : Technotification

 

While C and C++ are still considered to be a holy grail of the programming languages, new and in-demand programming languages, frameworks have risen to fame. Developers need to upgrade their skills constantly to keep up with the current market. There are the many ways to know the popularity of a programming language but we think that Indeed.com is the best source to know about developer trends.

Indeed.com has compiled a list of programming languages that are going to be extensively used in 2018.

Highly Demanded Programming Languages in 2018
1. Java
The job postings for Java Developers have reduced significantly compared to 2017 but the language is extremely well established. The continued support from Oracle is keeping the language in Loop. Furthermore, Java is still being used in billions of devices worldwide and is currently able to run on the most of the Hardware platforms. 90% of the fortune’s top 500 companies are using Java as the server-side programming language. Check: Five Reasons: Why Java is one of the best programming Languages
2.Python
Python has grown its popularity on a great scale over the past two years because of the Machine Learning. Most of the standard open source ML Frameworks are written in Python which placed the language in second place. The popularity of Python mostly come from the field of Data Mining and Artificial Intelligence. Check: 5 Best Programming Languages for Artificial Intelligence Systems

3. JavaScript
We all know that the JavaScript is being used everywhere these days. It is being used by more than 80% of the developers and 95% of the websites on the Internet. Facebook and Google are also backing JavaScript with their own frameworks and libraries such as ReactJS and Angular Frameworks. With Node.js it’s being used for Back-end programming as well. Tensorflow, The AI development library is also created on the top of Javascript. We also think that JavaScript’s popularity won’t drop any sooner. Check: Top 5 Best Modern JavaScript Frameworks for Web and App Development
4.C++
C++ is an old school programming language yet it is used by many developers worldwide. C++ is the extension of the C Programming language. It is primarily used in Game Development, Embedded Firmware. Many programmers find C++ hard to learn compared to Python or other loosely typed programming languages. Check: C vs C++ : Which Programming language is better?

5.C#
Pronounced as “C Sharp” went down in demand from the previous year. The Programming language was designed by Microsoft as an Object Oriented Programming language to make the development of Microsoft Apps easier. C# can also be used for Game Development e.g. Unity Engine uses C# for it Engine. So if you are aspiring video game developer, C# is the programming language to look out for. Check: Top 10 Programming Languages Used By Coders On Github

6.PHP
PHP is a Recursive acronym for HyperText Preprocessor which is primarily used as a server-side scripting language. Most of the Developers use PHP as a backend server-side programming language to interact with the database. Check: PHP vs Python : Which one is the Best Server-Side Programming?

These are the well established and old programming languages. So, it’s obvious that they will remain in high-demand because major applications still use them. For new applications, here’s the list of 6 New Programming Languages you should get to know. Let’s discuss some of them.

Swift
Swift is was designed by Apple for iOS and Mac Apps. The programming language was released at WWDC 2014. While most of the iOS apps are powered by Objective-C, we think that Swift will Replace Objective-C very soon because of its ease of use and features.

Rust
Rust is termed as the most loved programming language in 2018 at StackOverflow Developer Trends. It is currently growing steadily as the programming language supports a lot of features and also offers speed since it a compiled programming language. Check: 5 Best Open Source Frameworks For Developers and Programmers

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