Aileen  Jacobs

Aileen Jacobs

1594167957

The Grind: Tech & Coding Weekly — Issue

News

Uber Buying Postmates

It appears Uber is looking to purchase the popular food delivery service Postmates for $2.65 Billion. The move comes at a time when the company is looking to expand Uber Eats in order to gain ground against rival DoorDash. BLOOMBERG

Internet Archive Gets Legal Help

In a previous issue, we covered how the Internet Archive — a non-profit supporting a giant trove of educational media — has been sued by multiple large publishing companies for loosening hold restrictions via the National Emergency Library initiative to combat the crisis caused by COVID-19. Now the Electronic Frontier Foundation and major legal law firm Durie Tangri are standing with the IA in what will surely be a long, drawn-out legal battle. TORRENTFREAK

Trump Targets Internet Censorship Law

When Twitter started adding disclaimers to Donald Trump’s tweets, it reignited the debate about how social media companies should handle controversial messages posted on their platform. The related law, Section 230, provides blanket protections for tech companies. It’s been foundational in creating the internet as we know it, but some argue it’s also due for a review. THE NEW YORKER

Bandcamp: Algorithm-Free Success

Online music store Bandcamp on how the human touch — not algorithms — is responsible for the continued success of the site. Unlike streaming services like Spotify or Tidal, Bandcamp exists as a place that puts the musician first. This approach gives the site more of a record store feel and allows the company to give much more back to the artists it serves. RESIDENTADVISER

#javascript #programming

What is GEEK

Buddha Community

The Grind: Tech & Coding Weekly — Issue
Tyrique  Littel

Tyrique Littel

1604008800

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:

static analysis workflow

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., 7Bob, 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

Wiley  Mayer

Wiley Mayer

1603904400

How to Prepare for a Coding Interview in 8 Weeks

As of this writing, the market is tough. We’ve been hit hard with a deadly

pandemic that left thousands of people unemployed. It’s layoffs everywhere and the companies are being conservative when it comes to

hiring.

Companies are not willing to hire people with no experience or people who they’ve to train.

Your first job in tech is the toughest, you’re competing

with virtually every new college grad and anyone who completed a boot

camp. I know it can be hard to even land an interview, for someone to

give you a chance to talk and demonstrate you could be valuable

employee.

Now, the chance of you getting an interview totally depends on how your resume compares to the job description. The more relevant it is to the

skills required, the better your chances of getting an interview.

To build your resume, I’d recommend https://thetechresume.com. It’s a nice read to follow the principles when it comes to building a tech resume.

Over the past few months, I’ve been collecting resources like videos,

websites, and taking notes to prepare for coding interviews.

In that process, I made an 8 weeks study guide curated of important data

structure resources to prepare for tech interviews and honestly this

study guide was helpful to me to know what to study every day and in

following a routine for my job search.

Why 8 weeks?

If you’re serious about preparing for a tech interview then 8 weeks is the

minimum to be given to prepare thoroughly for a tech interview. I know

there are few who would cram up pools of content in a week or two. But, I

believe that is not a realistic or sensible approach.

Tech interviews can be intense and most companies expect you to solve problems or go through a data structure topic in detail.

Now, My study guide with resources will eat up the entire blog space. So,

Instead of straight-up dumping down the content all together, I racked

my brains on how to deliver the content in the most effective way

possible to ensure the habit of consistency and dedication stays intact

during the interview preparation process.

In this blog post, I would give you what to cover each week. If you’re

interested to know what resources to refer to when covering each topic then I’d recommend subscribing to the newsletter https://thedailycoding.com in which you’ll receive one email daily about the concept and the resources to practice.

If you believe you can find resources to relevant topics on your own then

here’s how you should plan to cover each topic every week.

#coding-interviews #software-development #job-interview #job-search #coding #latest-tech-stories #coding-interview-tips #coding-job-interview-advice

Samanta  Moore

Samanta Moore

1621137960

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

Houston  Sipes

Houston Sipes

1604088000

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

Fannie  Zemlak

Fannie Zemlak

1604048400

Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

Basic building blocks of Softagram

  • Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

  • Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

  • Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company