Programming Languages: A Usage-based Statistical Analysis and Visualization

Orlowska, Aleksandra; Chrysoulas, Christos; Jaroucheh, Zakwan; Liu, Xiaodong

Abstract

Understanding the popularity, and its trend, of programming languages can be important to anticipate which languages are best studied for future use, which are widely supported for use in enterprise projects, and investigate which skills are easiest to recruit for. In addition, popularity of languages used together can indicate tight communities of languages. This project aims to discover the current state of language popularity and use historical data to understand their respective trends. Data was sourced and processed from GitHub project language data and StackOverflow question tags be-tween 2008 and 2020. This data is then collated into visualizations with the aim of understanding popularity of languages in isolation, and in tandem. Several different visualizations were utilized, each giving different perspectives on the data. The findings were analyzed and discussed with a view to adequately identify the current trends in programming languages. From the results, it is understood that JavaScript is leading programming language in popularity in isolation. HTML and CSS are languages are commonly used in tandem with JavaScript.

Citation

Orlowska, A., Chrysoulas, C., Jaroucheh, Z., & Liu, X. (2021, March). Programming Languages: A Usage-based Statistical Analysis and Visualization. Presented at ICISS 2021: 2021 The 4th International Conference on Information Science and Systems, Edinburgh

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICISS 2021: 2021 The 4th International Conference on Information Science and Systems
• Start Date: Mar 17, 2021
• End Date: Mar 19, 2021
• Acceptance Date: Apr 2, 2021
• Online Publication Date: Mar 17, 2021
• Publication Date: Mar 17, 2021
• Deposit Date: Nov 26, 2021
• Publisher: Association for Computing Machinery (ACM)
• Pages: 143-148
• Book Title: ICISS 2021: 2021 The 4th International Conference on Information Science and Systems
• ISBN: 978-1-4503-8913-6
• DOI: https://doi.org/10.1145/3459955.3460614
• Keywords: JavaScript, GitHub, Stack Overflow, Statistics, Programming languages
• Public URL: http://researchrepository.napier.ac.uk/Output/2803047