Students learning computer science are met with numerous challenges beyond just the course material. Complex tooling, confusing setup, and overwhelming systems can frustrate students and teachers alike before even starting their coursework.
To explore these challenges, we have met with numerous students and professors around the world to explore these challenges.
Let’s face it: people are impatient by nature and most likely want things to happen faster in their lives. I would apply the same to code learners. Students, when starting to learn programming, first wonder how to speed up the training and make a career as a programmer as soon as possible.
I am not the one who convinces everyone that learning to program is a lightning-fast journey — the other thing is that it is not as difficult as people think. All boils down to interest, passion, regular practice, and patience, of course. I also often recommend different online and offline resources to my students to make their learning process easier, more effective, and faster. And in this post, I will share a few tips with you.
These tips are not magic pills and probably won’t make you a professional developer in a week, but they will definitely make your life easier, while the learning process won’t be that intimidating, vague, and boring. Besides, following all of them together may speed up training.
So, without any further delay, let’s get to them.
No matter how simple a new subject is, it still requires you to consolidate your knowledge. Starting to play with code soon after you’ve completed the next section helps you learn the given concepts faster and feel confident when writing your first line of code. Fortunately, the web is full of platforms, where you can start practicing shortly.
Let’s consider them in detail.
#coding #learn-to-code #learning #programming #learning-to-code #machine-learning
Are you an Arctic Code Vault Contributor or have seen someone posting about it and don’t know what it is. So let’s take a look at what is an Arctic Code Vault Contributor and who are the ones who gets this batch.
GitHub, the world’s largest open-source platform for software and programs has safely locked the data of huge value and magnitude in a coal mine in Longyearbyen’s Norwegian town in the Arctic region.
Back in November 2019, GitHub Arctic Code Vault was first announced.
The GitHub Arctic Code Vault is a data repository preserved in the Arctic
World Archive (AWA), a very-long-term archival facility 250 meters deep in the permafrost of an Arctic mountain. The archive is located in a decommissioned coal mine in the Svalbard archipelago, closer to the North Pole than the Arctic Circle.
Last year, GitHub said that it plans to capture a snapshot of every active
public repository on 02/02/2020 and preserve that data in the Arctic
The project began on February 2, when the firm took a snapshot of all of
GitHub’s active public repositories to store them in the vault. They initially intended to travel to Norway and personally escort the world’s open-source technology to the Arctic but their plans were derailed by the global pandemic. Then, they had to wait until 8 Julyfor the Arctic Data Vault data to be deposited.
GitHub announced that the code was successfully deposited in the Arctic Code Vault on July 8, 2020. Over the past several months, GitHub worked
with its archive partners Piql to write the 21TB of GitHub repository data to 186 reels of piqlFilm (digital photosensitive archival film).
GitHub’s strategic software director, Julia Metcalf, has written a blog post
on the company’s website notifying the completion of GitHub’s Archive Program on July 8th. Discussing the objective of the Archive Program, Metcalf wrote “Our mission is to preserve open-source software for future generations by storing your code in an archive built to last a thousand years.”
The Arctic Code Vault is only a small part of the wider GitHub Archive
Program, however, which sees the company partner with the Long Now
Foundation, Internet Archive, Software Heritage Foundation, Microsoft
Research and others.
Svalbard has been regulated by the international Svalbard Treaty as a demilitarized zone. Home to the world’s northernmost town, it is one of the most remote and geopolitically stable human habitations on Earth.
The AWA is a joint initiative between Norwegian state-owned mining company Store Norske Spitsbergen Kulkompani (SNSK) and very-long-term digital preservation provider Piql AS. AWA is devoted to archival storage in perpetuity. The film reels will be stored in a steel-walled container inside a sealed chamber within a decommissioned coal mine on the remote archipelago of Svalbard. The AWA already preserves historical and cultural data from Italy, Brazil, Norway, the Vatican, and many others.
The 02/02/2020 snapshot archived in the GitHub Arctic Code Vault will
sweep up every active public GitHub repository, in addition to significant dormant repos.
The snapshot will include every repo with any commits between the announcement at GitHub Universe on November 13th and 02/02/2020,
every repo with at least 1 star and any commits from the year before the snapshot (02/03/2019 – 02/02/2020), and every repo with at least 250 stars.
The snapshot will consist of the HEAD of the default branch of each repository, minus any binaries larger than 100KB in size—depending on available space, repos with more stars may retain binaries. Each repository will be packaged as a single TAR file. For greater data density and integrity, most of the data will be stored QR-encoded and compressed. A human-readable index and guide will itemize the location of each repository and explain how to recover the data.
The company further shared that every reel of the archive includes a copy
of the “Guide to the GitHub Code Vault” in five languages, written with input from GitHub’s community and available at the Archive Program’s own GitHub repository.
#github #open-source #coding #open-source-contribution #contributing-to-open-source #github-arctic-code-vault #arctic-code-vault #arctic-code-vault-contributor
Everyone makes mistakes, not just beginners, but even professionals. This article goes over a dozen common mistakes that Java newbies and newcomers make and how to avoid them. Have you or your colleagues made any of these common Java mistakes early in your career?
Everyone makes mistakes, not only learners or beginners but professionals. As a programming course, the CodeGym team often collects mistakes of newbies to improve our auto validator. This time we decided to interview experienced programmers about mistakes in Java they made closer to their careers start or noticed them among their young colleagues.
We collected their answers and compiled this list of dozen popular mistakes Java beginners make. The order of errors is random and does not carry any special meaning.
#java #learn-java #java-programming #beginners #beginners-to-coding #learning-to-code #learn-to-code #learn-to-code-java
In the fall of 2012, I walked into my graduate advisor’s office and asked her which computer science class she recommended for me to enroll in. I explained that I was a complete novice in programming. She suggested Introduction to C Programming.
After attending a few lectures, I discover that the majority of the students I spoke to in this introductorycourse had some prior experience in programming.
Six weeks and 80 hours of work later, I dropped the course.
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.”
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.
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:
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.,
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:
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