It is time to talk about the state of software engineering in 2020 and beyond. Software engineering has seen explosive growth over the last 20 years, and it seems to be keeping that momentum up. According to Fortune data, total revenue of top 15 technology companies in the world was a record 1.67 Trillion US Dollars in 2019, which is up 2% from 2018. There are more software companies than ever now. In addition, existing non-software companies are introducing more software components into their products, anything from cars to washing machines. The future is software, but not all software is created equal. Identifying the most promising and fastest-growing areas of software can help you take off your career and projects. Investing in a growing area helps you to find a job easier and get paid better, and helps you in finding funding for your projects. So, let us not waste time and get right into it. This video uses GitHub Octoverse data as the basis, plus my experiences and observations as a software engineer with a decade of experience. I will also talk about the topics listed in the outline below.
Outline of this video:
• Overview
• Growth of Programming
• Growth of Open-Source
• Top Libraries
• Trending Projects
• Top Programming Languages
• Data Science and Machine Learning
• Cloud Computing, DevOps, and Security
• Conclusion
Resources referenced in this video:
• GitHub Octoverse: octoverse.github.com
• Wikipedia: List of largest technology companies by revenue: en.wikipedia.org/wiki/List_of_largest_technology_companies_by_revenue
• Stack Overflow Annual Developer Survey: insights.stackoverflow.com/survey
• Hired: State of Software Engineers: hired.com/state-of-software-engineers
If you want to read or contribute, you can find this guide on:
• quanticdev.com/articles/software-engineering-in-2020
My "How to Correctly Use GitHub for Software Development - My Full Dev Workflow Using GitHub" video:
• kzread.info/dash/bejne/qYZ1q7tmgq-5ptI.html
Software Engineering Playlist:
• kzread.info/head/PLlPRnMzqjADpNe-NMMAm9cGkLEVlDZWvh
- - - - - - - - - - -
quanticdev
quantic_dev
quanticdev.com
- - - - - - - - - - -
Abstract:
Growth of Programming
10M new developers joined the GitHub in 2019. These new developers contributed to 44M+ repositories from all countries around the world. 80% of all code commits were made from outside the US. Again, the tech industry is growing very fast outside of the US too, and the GitHub data indicates that.
Growth of Open-Source
On average, each open source project on GitHub had contributors from 41 different countries and regions. This again points to the pace of growth outside of the US. After the United States, open source use picked up speed in China, India, and Germany. Developers in China forked and cloned 48% more projects than last year.
Top Programming Languages
It is no shock that the programming language that is powering most of Web, JavaScript, is still number one. I expect Python to surpass JavaScript at some point, but we will have to wait and see exactly when. In fact, outside of GitHub, Python might already be #1 programming language. As always, Java is near the top along with PHP and C++. What is surprising to me is the meteoric rise of TypeScript. I was predicting that once all good features of TypeScript end up in JavaScript itself, it would be discarded just like CoffeeScript and many others. Instead, it seems to be powering through.
Data Science and Machine Learning
2019 was the year of data science and machine learning. I fully expect it to continue in the same trajectory for almost the entirety of this decade. There is so much value in analyzing and understanding mass quantities of data. Especially coupled with machine learning, the value generated by these two will possibly outpace all other branches of computer science. I would say the only thing that can dethrone data science and ML would be a breakthrough in quantum computing. Otherwise, it is a good idea to invest some time into the hottest branch of computer science. As I always say, if you invest in a technology that is growing, you can grow with it.
Cloud Computing, DevOps, and Security
Complexity and scale of software systems are increasing. In addition, specific tasks like machine learning require unconventional server hardware like TPUs, namely tensor processing units, which are custom-designed hardware to accelerate machine learning tasks. This results in an immense in-house effort to accommodate servers and maintain them manually. The most sensible way around this problem is using DevOps tools to automate the deployment and maintenance of your servers. It is also reasonable to move those servers to cloud providers so you will not ever have to see another bare-metal server again.