Measuring programming language popularity

There has never been a better time to live in the numbers world. Although always surrounded by numbers, we’ve never embraced or enjoyed it as much as we do now. Consequently, we tend to think that we can measure everything and anything. Be it happiness, mindfulness, health, or the more obvious quantities like calories, steps, or preferences. Yet, although there is hardly more quantifiable world than that of the IT, it is still hard to determine which programming language is the most popular.

First of all, “popularity” itself is an elusive idea. It may mean that a language is used more extensively or that is takes up the most time to use or learn. It may be linked to the prevalence of applications that require a certain language. Since there’s no consensus regarding the idea of popularity, there are various methods devoted to measuring which language is the most common according to different understandings of what programming languages “popularity” entails.

Some analysts claim that what matters is how many times a language is mentioned in job offers or in job titles. Those more concentrated on science count how many books on a given language are sold or published annually. Code-lovers count the numbers of lines committed in a given language. Work-oriented researchers take into account the number of completed projects. Still, the most common idea is to count the searches.

While Google Trends counts how many times a language name is looked up using web searches, it is PYPL that takes it one step further. PYPL, that is PopularitY of Programming Language, analyzes the raw data from Google Trends (starting from 2004) and focuses on language tutorials. The theory behind is that you’ll know which language to study or use thanks to the ”collective wisdom” of the users worldwide. What’s hot right now? java, Python (+6.5% in 5 years) and PHP (-4.8%).

A bit more established – active since 2001 – TIOBE programming community index may seem more comprehensive as far as its sources go. It covers Google, Google Blogs, Wikipedia, MSN, Yahoo, Baidu and even YouTube. TIOBE counts the usage of certain phrases (language names) and updates the data every month, so you can see the trends over time. It turns out that C, Java or C++ usually win this particular web-search-based trophy.

Both PYPL and TIOBE have been criticized for being flawed. The TIOBE Index counts the number of web pages with the language name, but does not count how many people actually read those pages, therefore it's more reactive than active. PYPL uses Google Trends to find out what the people want to know instead of what they end up finding. Still, it has been criticized for its assumption that users always enter “tutorial” in the search engine, instead of just writing, for instance, “PHP”, “python jobs” or "learn Java".

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