CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode.

It consists of programming problems, from a variety of sources.

Problems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.

Dataset Card for The Stack

  Changelog

Release Description

v1.0 Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB in size.

v1.1 The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack.

This repository contains programming data collected from 15 students during November and December of 2019 at Bielefeld University. Students were asked to implement gradient descent. Note that this data set contains only source code snapshots and neither timestamps nor personal information. All students programmed in a web environment, which is also contained in this repository.

As of 2022, JavaScript and HTML/CSS were the most commonly used programming languages among software developers around the world, with more than 63.6 percent of respondents stating that they used JavaScript and just around 53 percent using HTML/CSS. Python, SQL, and TypeScript rounded out the top five most widely used programming languages around the world.

Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

These files accompany the book entitled: An Introduction to Programming Languages: Simultaneous Learning in Multiple Coding Environments. This work is an introductory textbook in several computer languages. It describes the most well-known and popular programming environments such as: C#, C++, Java, JavaScript, PERL, PHP, Python, Ruby, and Visual Basic (VB) or Visual Basic for Applications (VBA). Therefore, the main objective of this unique guide is to provide code examples reflected in these nine computer languages. Readers can easily understand the connection and universality between the syntax of different environments and be adept at translating code. This learning experience can be ideal for upper-undergraduate introductory courses, researchers, doctoral students, and sociologists or engineers charged with implementing data analysis. Graphical illustrations are used for technical details about the computation examples to aid in an in-depth understanding of their inner workings. Moreover, the book contains original material that has been class-tested by the author and numerous cases are examined. Readers will also benefit from the inclusion of: a) Historical and philosophical perspectives on the past, present and future of computer languages. b) A total of 448 additional files freely available online, from which a total of 44 files are poster presentations (i.e. PowerPoint and PDF files). c) A total of 404 code examples reflected in nine computer languages, namely: C#, C++, Java, JavaScript, PERL, PHP, Python, Ruby and VB. This work first begins with a general introduction to history and presents the natural inevitable pathway from mechanical automatons to present electronic computers. Following this historical introduction, an in-detail look is made on philosophical questions, implementations, entropy and life. More often than not, there is a genuine amazement of the younger generations regarding the advancement of computer technology. Historical events that led to the development of technologies have been distilled down to the essence. However, the essence of any story is made with massive loss of detailed information. The essence of essences even more so. Over time, the lack of detail leads to a collective amnesia that can prevent us from understanding the naturalness by which technology has evolved. Thus, new constructs are always built upon older constructs to fit the evolutionary chain of technological progress, which boils down to the same fundamental rules as biological evolution. In the first stage, this book discusses the natural path of programming constructs by starting from time immemorial and ending with examples up to the present times. In the end, naturally driven constructs of all kinds also drive our society today. In the second part, the emphasis is made on the technical side where a total of nine computer languages are used simultaneously for mirrored examples. Simultaneous learning of multiple computer languages can be regarded as an asset in the world of science and technology. Thus, the reader can get used to the majority of known programming or scripting languages. Moreover, a basic knowledge of software implementation in several computer languages, even in an introductory way, helps the versatility and adaptability of the reader to new situations that may arise in industry, education, or research. Thus, this work is meant to bring a more concrete understanding of the similarities and differences between computer languages.

Paul A. Gagniuc. An Introduction to Programming Languages: Simultaneous Learning in Multiple Coding Environments. Synthesis Lectures on Computer Science. Springer International Publishing, 2023, pp. 1-280.

The APPS dataset consists of problems collected from different open-access coding websites such as Codeforces, Kattis, and more. The APPS benchmark attempts to mirror how humans programmers are evaluated by posing coding problems in unrestricted natural language and evaluating the correctness of solutions. The problems range in difficulty from introductory to collegiate competition level and measure coding ability as well as problem-solving.

The Automated Programming Progress Standard, abbreviated APPS, consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each. In the test set, every problem has multiple test cases, and the average number of test cases is 21.2. Each test case is specifically designed for the corresponding problem, enabling us to rigorously evaluate program functionality.

Dataset Card for "programming-languages-keywords"

Structured version of https://github.com/e3b0c442/keywords Generated using: r = requests.get("https://raw.githubusercontent.com/e3b0c442/keywords/main/README.md") keywords = r.text.split("### ")[1:] keywords = [i for i in keywords if not i.startswith("Sources")] keywords = {i.split(" ")[0]:[j for j in re.findall("[a-zA-Z]*", i.split(" ",1)[1]) if j] for i in keywords} keywords =… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/programming-languages-keywords.

programming languages statistics: The tech market which is also booming along with digital marketing is pretty good for a better income source. The tech market has many other things including programming languages. Programming languages are the basis for the formation of various websites, games, software, mobile applications, etc... There are nearly 9,000 programming languages around the world with each language with its own feature. In this most popular programming language statistics, we will have a look at statistical information and general knowledge about worldwide available various programming languages. Programming Languages Statistics (Editor’s Choice) There are 8,945 programming languages as stated by most popular Programming languages statistics. As of 2022, JavaScript is one of the most popular programming languages as around 47.86% of recruiters are demanding JavaScript language skills. A basic python developer earns between $70,000 to $1,00,00 a year. As per the most popular programming languages statistics Python has ranked number 1 in the United States of America, India, Germany, France, and the United Kingdom

The most popular programming language used in the past 12 months by software developers worldwide is JavaScript as of 2023, according to 65 percent of the software developers surveyed. This is followed by Python at 54 percent of the respondents surveyed.

Non-binary files from GitHub repositories and list its source and license in sources.json

Dataset has 6 columns, and you can read text file using file_path column

There are files for Assembly, Awk, Batchfile, C, C#, C++, CMake, CSS, CSV, Clojure, CoffeeScript, Common Lisp, D, Dart, Diff, Dockerfile, ERB, Elixir, Erlang, Fortran, GAS, GLSL, Go, Gradle, Groovy, HTML, Haskell, Java, Javascript, Julia, Jupyter Notebook, Kotlin, LLVM, Less, Limbo, Lisp, Lua, Makefile, Markdown, PHP, Pascal, PowerShell, Prolog, Puppet, Python, Q#, Ruby, Rust, SCSS, SQL, SVG, Scala, Scheme, Shell, Swift, TeX, Text, TypeScript, XML, YAML and more.

I know that HTML is NOT a programming language. it doesn't matter because it even has JSON and Text. 😄

Python Programming Puzzles (P3) is an open-source dataset where each puzzle is defined by a short Python program , and the goal is to find an input which makes output "True". The puzzles are objective in that each one is specified entirely by the source code of its verifier, so evaluating is all that is needed to test a candidate solution. They do not require an answer key or input/output examples, nor do they depend on natural language understanding.

The dataset is comprehensive in that it spans problems of a range of difficulties and domains, ranging from trivial string manipulation problems that are immediately obvious to human programmers (but not necessarily to AI), to classic programming puzzles (e.g., Towers of Hanoi), to interview/competitive-programming problems (e.g., dynamic programming), to longstanding open problems in algorithms and mathematics (e.g., factoring). The objective nature of P3 readily supports self-supervised bootstrapping.

CodeQA is a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated. CodeQA contains a Java dataset with 119,778 question-answer pairs and a Python dataset with 70,085 question-answer pairs.

Description from: CodeQA: A Question Answering Dataset for Source Code Comprehension

Software development is a continuous decision-making process that mainly relies on the software engineer's experience and intuition. One of the essential decisions in the early stages of the process is selecting the best fitting programming language based on the project requirements. A significant number of criteria, such as developer availability and consistent documentation, besides potential programming languages in the market, lead to a challenging decision-making process. A decision model is required to analyze the selection problem using systematic identification and evaluation of potential alternatives for a development project. Method: Recently, we introduced a framework to build decision models for technology selection problems in software production. Furthermore, we designed and implemented a decision support system that uses such decision models to support software engineers with their decision-making problems. This study presents a decision model based on the framework for the programming language selection problem. Results: The decision model has been evaluated through seven real-world case studies at seven software development companies. The case study participants declared that the approach provides significantly more insight into the programming language selection process and decreases the decision-making process's time and cost. Conclusion: With the knowledge available through the decision model, software engineers can more rapidly evaluate programming languages. Having this knowledge readily available supports software engineers in making more efficient and effective decisions that meet their requirements and priorities.

Introduction

Most Popular Programming Languages Statistics: Programming languages allow us to communicate with computers, enabling the creation of scripts, programs, and applications. Each language has its syntax, symbols, and keywords for writing code. Even a small mistake, like a misplaced comma, can cause the code to fail. These languages are also essential for building websites. Each language has specific advantages and disadvantages when it comes to application, but with the right skills and techniques, coding can be enjoyable. Let's take a look at the most recent statistics for the most popular programming languages.

Question Paper Solutions of chapter Programming Basics of Programming Concept with Python, 1st Semester , Master of Computer Applications (2 Years)

Dataset is intended for studying how student programming styles and usage of IDE differs between students who plagiarise their homework and students who solve them honestly.Dataset includes homeworks submitted by students during two introductory programming courses (A and B) delivered during two years (2016 and 2017). A is delivered in C programming language, while B is delivered in C++. In addition to homeworks, dataset includes full traces of all student activity and keystrokes during homework development. These traces were generated by setting the IDE to "autosave" after 1 second of inactivity, after which the file was committed to a SVN repository. For size reason, these repositories were then processed into JSON files actually stored. In addition, IDE was configured to pass output from student programs, compiler, debugger, profiler and unit testing into separate invisible files which were also stored in this repository. Finally, dataset includes ground truth with homeworks which are assumed to be plagiarised because of high similarity and the fact that (one of) students failed to do "oral defense" of homework.

Dataset

This dataset was created by Ahmed Elsayed Taha

Contents

JavaScript was the most frequently used coding language in Russia, used by over 65 percent of the surveyed software companies in 2023. Furthermore, over one half of the companies reported to use Python and Java.

Dataset Card for CodeContests

  Dataset Summary

CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode. It consists of programming problems, from a variety of sources:

Site URL Source

Aizu https://judge.u-aizu.ac.jp CodeNet

AtCoder https://atcoder.jp CodeNet

CodeChef https://www.codechef.com description2code

Codeforces https://codeforces.com description2code and Codeforces

HackerEarth… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/code_contests.

Programming Software Market size was valued at USD 30.9 Billion in 2024 and is projected to reach USD 147.8 Billion by 2031, growing at a CAGR of 23.4% during the forecast period 2024-2031.

Global Programming Software Market Drivers

Technological Innovation: The market for programming software is primarily driven by technological advancements. The landscape is always changing due to advancements in programming languages, frameworks, and tools, which empower developers to produce increasingly complex and effective software solutions. The need for programming software that supports cutting-edge technologies like cloud computing, AI, machine learning, and the Internet of Things (IoT) is increasing.

Growing Need for Customized Solutions: Companies in a variety of sectors are depending more and more on software solutions made to meet their unique requirements. The need for programming tools that makes it possible for developers to quickly and effectively create highly customized apps is driven by this desire. The market is becoming more and more competitive, and this is driving up demand for programming tools that are both versatile and scalable.

Move Towards Open Source Software: Due to its affordability, adaptability, and collaborative nature, open source software has seen a sharp increase in popularity in recent years. Because of its accessibility and active community support, open source programming software is preferred by many developers and organizations. As a result, open source tools and frameworks are becoming more popular in the programming software market.

The use of DevOps principles, which prioritize cooperation between development and operations teams to expedite software delivery, is on the rise. These practices are being embraced by enterprises looking to increase their efficiency and agility. Programming software that enables smooth integration, automation, and continuous delivery inside the DevOps pipeline is in high demand due to this trend.

A Growing Focus on Security: As a result of the increase in cyberattacks and data leaks, security is now the top priority for businesses creating software solutions. Because of this, there is an increasing need for programming tools that support safe coding techniques and have strong security features. Programming frameworks and tools with a security focus are necessary to fix vulnerabilities and guarantee the integrity of software programs.

Transition to No-Code/Low-Code Development:
Because low-code/no-code development platforms make it possible for users with different degrees of technical expertise to construct apps quickly, they are democratizing software development. The demand for increased agility, lower development costs, and a quicker time to market is what’s driving this trend. Consequently, low-code/no-code tools are becoming more and more popular in the programming software market alongside conventional programming languages and frameworks.

Industry-Specific Requirements: The selection of programming software is influenced by the particular requirements and regulatory norms of various industries. Industry-specific standards and regulatory compliance are made easier by the need for programming tools in areas like finance, healthcare, and automotive, which have strict compliance requirements.

Global Economic variables: The market for programming software is also impacted by economic variables like GDP growth, investment trends, and geopolitical developments. While economic expansion can lead to higher investment in software development activities, economic downturns may result in reduced IT budgets and slower adoption of new technology.