How to Google - You Suck at Coding [2]

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Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

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

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import io

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import tokenize

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4

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

5

6

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

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    print(token)

Python

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

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

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

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

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

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

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

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

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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 tell if your code actually sucks...

There is no better moment for me than starting a brand new project.

Smells like new project spirit… (Whatever it means)

Starting a new project is funny. Everything seems to be in the right place. But as the projects grow and the deadlines come closer the things begin to boiling.

So, let’s talk about signals that can tell us if our code sucks and we how we can avoid that.

Everyone is afraid of adding or removing stuff

I guess we all have known at least one project that anyone wants to touch, or heard the phrase:

It works, don’t touch it!

Well, that’s not a good signal. I know there are complex projects, big projects, but if nobody in your team can touch it without breaking something, then there is something wrong with that code.

Code is like a garden, it needs to be treat and maintained, if it grows in size or complexity with no control, then will be harder to maintain and easily can get death.

Code grows out of control when there are no conventions to work in it, team practices, even solo practices are important to keep our code under control.

If you see yourself in a scenario where is hard to add things to your project, then you should rethink the whole thing.

Only the creator understands it (Sometimes even the creator can’t.)

If only one person in your team can understand a project, then that’s a problem and hopefully that person never gets sick or goes on vacation.

If you are working by yourself please don’t write overcomplicated code; in my experience simplicity is better; writing code that anyone can read is the right thing to do.

t is clear today may not be that clear in a couple of weeks, even for you.

Use comments on your code. Do not comment on every single line but put enough comments on the complicated and crucial parts.

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If you develop on javascript this is a great repo with good practices.

It is difficult to read

I have to insist on this. Simple is better; there is no need to show anyone how abstract you can be or how much you know the language. Keeping things simple is way much more productive than trying to show off your knowledge and skill.

Keep your code as readable as possible, simple as possible. Clear variable names, descriptive functions names, clear statements. This will save time for you and your team.

A good way to measure how readable your code is is to overcome the necessity of comments. If the code does not need many comments to describe it, then it means the code is readable enough.

Conclusion

The best code is not only the one that is fast and performant; the best code is also the one you enjoy working on. I’ve had nightmares of codebases that I had to work with, and I also have had codebases that I enjoy.

Coding is a team sport, and every member of the team must be able to play the game, so write for the team.

#development #programming #software-development #coding #coding-skills #software-engineering #code-quality #code

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