Testbed for Code Size Minimization Strategies in The Rust

The Rust Programming Language

This is the main source code repository for Rust. It contains the compiler, standard library, and documentation.

Note: this README is for users rather than contributors. If you wish to contribute to the compiler, you should read the Getting Started section of the rustc-dev-guide instead.

Quick Start

Read "Installation" from The Book.

Installing from Source

The Rust build system uses a Python script called x.py to build the compiler, which manages the bootstrapping process. It lives in the root of the project.

The x.py command can be run directly on most systems in the following format:

./x.py <subcommand> [flags]

This is how the documentation and examples assume you are running x.py.

Systems such as Ubuntu 20.04 LTS do not create the necessary python command by default when Python is installed that allows x.py to be run directly. In that case you can either create a symlink for python (Ubuntu provides the python-is-python3 package for this), or run x.py using Python itself:

# Python 3
python3 x.py <subcommand> [flags]

# Python 2.7
python2.7 x.py <subcommand> [flags]

More information about x.py can be found by running it with the --help flag or reading the rustc dev guide.

Building on a Unix-like system

  1. Make sure you have installed the dependencies:
  • g++ 5.1 or later or clang++ 3.5 or later
  • python 3 or 2.7
  • GNU make 3.81 or later
  • cmake 3.13.4 or later
  • ninja
  • curl
  • git
  • ssl which comes in libssl-dev or openssl-devel
  • pkg-config if you are compiling on Linux and targeting Linux

2.   Clone the source with git:

git clone https://github.com/rust-lang/rust.git
cd rust

3.   Configure the build settings:

The Rust build system uses a file named config.toml in the root of the source tree to determine various configuration settings for the build. Copy the default config.toml.example to config.toml to get started.

cp config.toml.example config.toml

If you plan to use x.py install to create an installation, it is recommended that you set the prefix value in the [install] section to a directory.

Create install directory if you are not installing in default directory

4.   Build and install:

./x.py build && ./x.py install

When complete, ./x.py install will place several programs into $PREFIX/bin: rustc, the Rust compiler, and rustdoc, the API-documentation tool. This install does not include Cargo, Rust's package manager. To build and install Cargo, you may run ./x.py install cargo or set the build.extended key in config.toml to true to build and install all tools.

Building on Windows

There are two prominent ABIs in use on Windows: the native (MSVC) ABI used by Visual Studio, and the GNU ABI used by the GCC toolchain. Which version of Rust you need depends largely on what C/C++ libraries you want to interoperate with: for interop with software produced by Visual Studio use the MSVC build of Rust; for interop with GNU software built using the MinGW/MSYS2 toolchain use the GNU build.


MSYS2 can be used to easily build Rust on Windows:

Grab the latest MSYS2 installer and go through the installer.

Run mingw32_shell.bat or mingw64_shell.bat from wherever you installed MSYS2 (i.e. C:\msys64), depending on whether you want 32-bit or 64-bit Rust. (As of the latest version of MSYS2 you have to run msys2_shell.cmd -mingw32 or msys2_shell.cmd -mingw64 from the command line instead)

From this terminal, install the required tools:

# Update package mirrors (may be needed if you have a fresh install of MSYS2)
pacman -Sy pacman-mirrors

# Install build tools needed for Rust. If you're building a 32-bit compiler,
# then replace "x86_64" below with "i686". If you've already got git, python,
# or CMake installed and in PATH you can remove them from this list. Note
# that it is important that you do **not** use the 'python2', 'cmake' and 'ninja'
# packages from the 'msys2' subsystem. The build has historically been known
# to fail with these packages.
pacman -S git \
            make \
            diffutils \
            tar \
            mingw-w64-x86_64-python \
            mingw-w64-x86_64-cmake \
            mingw-w64-x86_64-gcc \

Navigate to Rust's source code (or clone it), then build it:

./x.py build && ./x.py install


MSVC builds of Rust additionally require an installation of Visual Studio 2017 (or later) so rustc can use its linker. The simplest way is to get the Visual Studio, check the “C++ build tools” and “Windows 10 SDK” workload.

(If you're installing cmake yourself, be careful that “C++ CMake tools for Windows” doesn't get included under “Individual components”.)

With these dependencies installed, you can build the compiler in a cmd.exe shell with:

python x.py build

Currently, building Rust only works with some known versions of Visual Studio. If you have a more recent version installed and the build system doesn't understand, you may need to force rustbuild to use an older version. This can be done by manually calling the appropriate vcvars file before running the bootstrap.

CALL "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvars64.bat"
python x.py build

Specifying an ABI

Each specific ABI can also be used from either environment (for example, using the GNU ABI in PowerShell) by using an explicit build triple. The available Windows build triples are:

  • GNU ABI (using GCC)
    • i686-pc-windows-gnu
    • x86_64-pc-windows-gnu
  • The MSVC ABI
    • i686-pc-windows-msvc
    • x86_64-pc-windows-msvc

The build triple can be specified by either specifying --build=<triple> when invoking x.py commands, or by copying the config.toml file (as described in Installing From Source), and modifying the build option under the [build] section.

Configure and Make

While it's not the recommended build system, this project also provides a configure script and makefile (the latter of which just invokes x.py).

make && sudo make install

When using the configure script, the generated config.mk file may override the config.toml file. To go back to the config.toml file, delete the generated config.mk file.

Building Documentation

If you’d like to build the documentation, it’s almost the same:

./x.py doc

The generated documentation will appear under doc in the build directory for the ABI used. I.e., if the ABI was x86_64-pc-windows-msvc, the directory will be build\x86_64-pc-windows-msvc\doc.


Since the Rust compiler is written in Rust, it must be built by a precompiled "snapshot" version of itself (made in an earlier stage of development). As such, source builds require a connection to the Internet, to fetch snapshots, and an OS that can execute the available snapshot binaries.

Snapshot binaries are currently built and tested on several platforms:

Platform / Architecturex86x86_64
Windows (7, 8, 10, ...)
Linux (kernel 2.6.32, glibc 2.11 or later)
macOS (10.7 Lion or later)(*)

(*): Apple dropped support for running 32-bit binaries starting from macOS 10.15 and iOS 11. Due to this decision from Apple, the targets are no longer useful to our users. Please read our blog post for more info.

You may find that other platforms work, but these are our officially supported build environments that are most likely to work.

Getting Help

The Rust community congregates in a few places:


If you are interested in contributing to the Rust project, please take a look at the Getting Started guide in the rustc-dev-guide.


The Rust Foundation owns and protects the Rust and Cargo trademarks and logos (the “Rust Trademarks”).

If you want to use these names or brands, please read the media guide.

Third-party logos may be subject to third-party copyrights and trademarks. See Licenses for details.

Download Details:
Author: paritytech
Source Code: https://github.com/paritytech/rustc-codesize-min
License: View license

#blockchain  #polkadot  #smartcontract  #substrate 

What is GEEK

Buddha Community

Testbed for Code Size Minimization Strategies in The Rust
Tyrique  Littel

Tyrique Littel


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


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:


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:



import io


import tokenize



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



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





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


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


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


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


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


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


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


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


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

Serde Rust: Serialization Framework for Rust


*Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.*

You may be looking for:

Serde in action

Click to show Cargo.toml. Run this code in the playground.


# The core APIs, including the Serialize and Deserialize traits. Always
# required when using Serde. The "derive" feature is only required when
# using #[derive(Serialize, Deserialize)] to make Serde work with structs
# and enums defined in your crate.
serde = { version = "1.0", features = ["derive"] }

# Each data format lives in its own crate; the sample code below uses JSON
# but you may be using a different one.
serde_json = "1.0"


use serde::{Serialize, Deserialize};

#[derive(Serialize, Deserialize, Debug)]
struct Point {
    x: i32,
    y: i32,

fn main() {
    let point = Point { x: 1, y: 2 };

    // Convert the Point to a JSON string.
    let serialized = serde_json::to_string(&point).unwrap();

    // Prints serialized = {"x":1,"y":2}
    println!("serialized = {}", serialized);

    // Convert the JSON string back to a Point.
    let deserialized: Point = serde_json::from_str(&serialized).unwrap();

    // Prints deserialized = Point { x: 1, y: 2 }
    println!("deserialized = {:?}", deserialized);

Getting help

Serde is one of the most widely used Rust libraries so any place that Rustaceans congregate will be able to help you out. For chat, consider trying the #rust-questions or #rust-beginners channels of the unofficial community Discord (invite: https://discord.gg/rust-lang-community), the #rust-usage or #beginners channels of the official Rust Project Discord (invite: https://discord.gg/rust-lang), or the #general stream in Zulip. For asynchronous, consider the [rust] tag on StackOverflow, the /r/rust subreddit which has a pinned weekly easy questions post, or the Rust Discourse forum. It's acceptable to file a support issue in this repo but they tend not to get as many eyes as any of the above and may get closed without a response after some time.

Download Details:
Author: serde-rs
Source Code: https://github.com/serde-rs/serde
License: View license

#rust  #rustlang 

Samanta  Moore

Samanta Moore


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


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


  • Readability
  • Maintainability
  • Code Quality
  • Premature Optimization


  1. Refactor the code
  2. Use better names


  • Optimized loops


  • Optimized code for low-level operations.

Sample Code


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

	  for (i = 0; n >= 2; ) {
	     if(n % d == 0){
	       n /= d;
	  return f;


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

	    if(remainder % divisor === 0){
	       remainder = remainder/ divisor;
	  return factors;


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


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