Loading and running WebAssembly code

To use WebAssembly in JavaScript, you first need to pull your module into memory before compilation/instantiation. This article provides a reference for the different mechanisms that can be used to fetch WebAssembly bytecode, as well as how to compile/instantiate then run it.

What are the options?

WebAssembly is not yet integrated with <script type='module'> or ES2015 import statements, thus there is not a path to have the browser fetch modules for you using imports.

The older WebAssembly.compile methods require you to create an ArrayBuffer containing your WebAssembly module binary after fetching the raw bytes, and then compile/instantiate it. This is analogous to new Function(string), except that we are substituting a string of characters (JavaScript source code) with an array buffer of bytes (WebAssembly source code).

The newer WebAssembly.compileStreamingmethods are a lot more efficient — they perform their actions directly on the raw stream of bytes coming from the network, cutting out the need for the ArrayBuffer step.

So how do we get those bytes into an array buffer and compiled? The following sections explain.

Using Fetch

Fetch is a convenient, modern API for fetching network resources.

The quickest, most efficient way to fetch a wasm module is using the newer WebAssembly.instantiateStreaming() method, which can take a fetch() call as its first argument, and will handle fetching, compiling, and instantiating the module in one step, accessing the raw byte code as it streams from the server:

WebAssembly.instantiateStreaming(fetch('simple.wasm'), importObject)
.then(results => {
  // Do something with the results!
});

If we used the older WebAssembly.instantiate() method, which doesn’t work on the direct stream, we’d need an extra step of converting the fetched byte code to an ArrayBuffer, like so:

fetch('module.wasm').then(response =>
  response.arrayBuffer()
).then(bytes =>
  WebAssembly.instantiate(bytes, importObject)
).then(results => {
  // Do something with the results!
});

Aside on instantiate() overloads

The WebAssembly.instantiate() function has two overload forms — the one shown above takes the byte code to compile as an argument and returns a promise that resolves to an object containing both the compiled module object, and an instantiated instance of it. The object looks like this:

{
  module : Module // The newly compiled WebAssembly.Module object,
  instance : Instance // A new WebAssembly.Instance of the module object
}

Note: Usually we only care about the instance, but it’s useful to have the module in case we want to cache it, share it with another worker or window via postMessage(), or simply create more instances.

Running your WebAssembly code

Once you’ve got your WebAssembly instance available in your JavaScript, you can then start using features of it that have been exported via the WebAssembly.Instance.exports property. Your code might look something like this:

WebAssembly.instantiateStreaming(fetch('myModule.wasm'), importObject)
.then(obj => {
  // Call an exported function:
  obj.instance.exports.exported_func();

  // or access the buffer contents of an exported memory:
  var i32 = new Uint32Array(obj.instance.exports.memory.buffer);

  // or access the elements of an exported table:
  var table = obj.instance.exports.table;
  console.log(table.get(0)());
})

Using XMLHttpRequest

XMLHttpRequest is somewhat older than Fetch, but can still be happily used to get a typed array. Again, assuming our module is called simple.wasm:

  1. Create a new XMLHttpRequest() instance, and use its open() method to open a request, setting the request method to GET, and declaring the path to the file we want to fetch.
  2. The key part of this is to set the response type to 'arraybuffer' using the responseType property.
  3. Next, send the request using XMLHttpRequest.send().
  4. We then use the onload event handler to invoke a function when the response has finished downloading — in this function we get the array buffer from the response property, and then feed that into our WebAssembly.instantiate() method as we did with Fetch.

The final code looks like this:

request = new XMLHttpRequest();
request.open('GET', 'simple.wasm');
request.responseType = 'arraybuffer';
request.send();

request.onload = function() {
  var bytes = request.response;
  WebAssembly.instantiate(bytes, importObject).then(results => {
    results.instance.exports.exported_func();
  });
};

Note: You can see an example of this in action in xhr-wasm.html.

#javascript #webassembly #wasm

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Loading and running WebAssembly code
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

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

Vincent Lab

Vincent Lab

1605176074

Let's Talk About Selling Your Code

In this video, I’ll be talking about when do I think code is ready to be sold.

#should you sell your code? #digital products #selling your code #sell your code #should you sell your code #should i sell my code