Top 10 VS Code Updates You Don't Know About!!

Here are the top 10 updates to VS Code that have happened in the first half of 2020 (January - July). A few of these I had no idea about. So hopefully these will be new to you and they will help you to code more efficiently. In case you didn’t know, Visual Studio Code is updated monthly. They are constantly adding new, awesome features.

Here’s a list of what we will cover:

• 00:00 Intro
• 00:20 Sponsor (Scrimba)
• 01:22 Draggable sash corners
• 02:49 Column selection mode
• 03:25 Convert to template string
• 03:47 GitHub authentication support
• 05:17 Flexible view & panel layout
• 06:07 Pin editor tabs
• 06:53 Edit complex settings
• 07:23 Change case in regex replace
• 08:24 New JavaScript debugger
• 08:50 New Course Update!!

#vscode #visual-studio #developer

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Top 10 Trending Technologies Must Learn in 2021 | igmGuru

Technology has taken a place of more productiveness and give the best to the world. In the current situation, everything is done through the technical process, you don’t have to bother about doing task, everything will be done automatically.This is an article which has some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top trending technologies followed in 2021 and its impression in the coming future in the world.

1. Data Science
   First in the list of newest technologies is surprisingly Data Science. Data Science is the automation that helps to be reasonable for complicated data. The data is produces in a very large amount every day by several companies which comprise sales data, customer profile information, server data, business data, and financial structures. Almost all of the data which is in the form of big data is very indeterminate. The character of a data scientist is to convert the indeterminate datasets into determinate datasets. Then these structured data will examine to recognize trends and patterns. These trends and patterns are beneficial to understand the company’s business performance, customer retention, and how they can be enhanced.

2. DevOps
   Next one is DevOps, This technology is a mixture of two different things and they are development (Dev) and operations (Ops). This process and technology provide value to their customers in a continuous manner. This technology plays an important role in different aspects and they can be- IT operations, development, security, quality, and engineering to synchronize and cooperate to develop the best and more definitive products. By embracing a culture of DevOps with creative tools and techniques, because through that company will gain the capacity to preferable comeback to consumer requirement, expand the confidence in the request they construct, and accomplish business goals faster. This makes DevOps come into the top 10 trending technologies.

3. Machine learning
   Next one is Machine learning which is constantly established in all the categories of companies or industries, generating a high command for skilled professionals. The machine learning retailing business is looking forward to enlarging to $8.81 billion by 2022. Machine learning practices is basically use for data mining, data analytics, and pattern recognition. In today’s scenario, Machine learning has its own reputed place in the industry. This makes machine learning come into the top 10 trending technologies. Get the best machine learning course and make yourself future-ready.

To want to know more click on Top 10 Trending Technologies in 2021

You may also read more blogs mentioned below

How to Become a Salesforce Developer

Python VS R Programming

The Scope of Hadoop and Big Data in 2021

#top trending technologies #top 10 trending technologies #top 10 trending technologies in 2021 #top trending technologies in 2021 #top 5 trending technologies in 2021 #top 5 trending technologies

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

• J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7, Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Guidelines for Java Code Reviews

Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

1. Follow Java Code Conventions

2. Replace Imperative Code With Lambdas and Streams

3. Beware of the NullPointerException

4. Directly Assigning References From Client Code to a Field

5. Handle Exceptions With Care

…

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

How to Find the Stinky Parts of Your Code (Part I)

of something that might be wrong. They are not rigid rules.

Code Smell 01 — Anemic Models

Your objects are a bunch of public attributes without behavior.

Photo by Stacey Vandergriff on Unsplash

Protocol is empty (with setters/getters).

If we ask a domain expert to describe an entity he/she would hardly tell it is ‘a bunch of attributes’.

Problems

• No Encapsulation.
• No mapping to real world entities.
• Duplicate Code
• Coupling

Solutions

• 1. Find Responsibilities.
• 2. Protect your attributes.
• 3. Hide implementations.
• 4. Delegate

Examples

• DTOs

Sample Code

	<?

	Class Window{
	  public height;
	  public width;

	  function getHeight(){
	    return $this->height;
	  }

	  function setHeight($height){
	    $this->height = $height;
	  }

	  function getWidth(){
	    return $this->width;
	  }

	  function setWidth($width){
	    $this->width = $width;
	  }

	}

Wrong

<?

	final Class Window{ 

	  function area(){
	    //...
	  }

	  function open(){
	    //..
	  }

	  function isOpen(){
	    //..
	  }

	}

Right

#code-smells #clean-code #refactoring #refactor-legacy-code #stinky-code #stinky-code-parts #pixel-face #hackernoon-top-story

How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

• Readability
• Maintainability
• Code Quality
• Premature Optimization

Solutions

1. Refactor the code
2. Use better names

Examples

• Optimized loops

Exceptions

• Optimized code for low-level operations.

Sample Code

Wrong

function primeFactors(n){
	  var f = [],  i = 0, d = 2;  

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       f[i++]=(d); 
	       n /= d;
	    }
	    else{
	      d++;
	    }     
	  }
	  return f;
	}

Right

function primeFactors(numberToFactor){
	  var factors = [], 
	      divisor = 2,
	      remainder = numberToFactor;

	  while(remainder>=2){
	    if(remainder % divisor === 0){
	       factors.push(divisor); 
	       remainder = remainder/ divisor;
	    }
	    else{
	      divisor++;
	    }     
	  }
	  return factors;
	}

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells