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sources/tech/20170901 An economically efficient model for open source software license compliance.md
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An economically efficient model for open source software license compliance
============================================================
### Using open source the way it was intended benefits your bottom line and the open source ecosystem.
![An economically efficient model for open source software license compliance](https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/LAW_EvidencedBasedIP_520x292_CS.png?itok=mmhCWuZR "An economically efficient model for open source software license compliance")
Image by : opensource.com
"The Compliance Industrial Complex" is a term that evokes dystopian imagery of organizations engaging in elaborate and highly expensive processes to comply with open source license terms. As life often imitates art, many organizations engage in this practice, sadly robbing them of the many benefits of the open source model. This article presents an economically efficient approach to open source software license compliance.
Open source licenses generally impose three requirements on a distributor of code licensed from a third party:
1. Provide a copy of the open source license(s)
2. Include copyright notices
3. For copyleft licenses (like GPL), make the corresponding source code available to the distributees
_(As with any general statement, there may be exceptions, so it is always advised to review license terms and, if necessary, seek the advice of an attorney.)_
Because the source code (and any associated files, e.g. license/README) generally contains all of this information, the easiest way to comply is to simply provide the source code along with your binary/executable application.
The alternative is more difficult and expensive, because, in most situations, you are still required to provide a copy of the open source licenses and retain copyright notices. Extracting this information to accompany your binary/executable release is not trivial. You need processes, systems, and people to copy this information out of the sources and associated files and insert them into a separate text file or document.
The amount of time and expense to create this file is not to be underestimated. Although there are software tools that may be used to partially automate the process, these tools often require resources (e.g., engineers, quality managers, release managers) to prepare code for scan and to review the results for accuracy (no tool is perfect and review is almost always required). Your organization has finite resources, and diverting them to this activity leads to opportunity costs. Compounding this expense, each subsequent release—major or minor—will require a new analysis and revision.
There are also other costs resulting from not choosing to release sources that are not well recognized. These stem from not releasing source code back to the original authors and/or maintainers of the open source project, an activity known as upstreaming. Upstreaming alone seldom meets the requirements of most open source licenses, which is why this article advocates releasing sources along with your binary/executable; however, both upstreaming and providing the source code along with your binary/executable affords additional economic benefits. This is because your organization will no longer be required to keep a private fork of your code changes that must be internally merged with the open source bits upon every release—an increasingly costly and messy endeavor as your internal code base diverges from the community project. Upstreaming also enhances the open source ecosystem, which encourages further innovations from the community from which your organization may benefit.
So why do a significant number of organizations not release source code for their products to simplify their compliance efforts? In many cases, this is because they are under the belief that it may reveal information that gives them a competitive edge. This belief may be misplaced in many situations, considering that substantial amounts of code in these proprietary products are likely direct copies of open source code to enable functions such as WiFi or cloud services, foundational features of most contemporary products.
Even if changes are made to these open source works to adapt them for proprietary offerings, such changes are often de minimis and contain little new copyright expression or patentable content. As such, any organization should look at its code through this lens, as it may discover that an overwhelming percentage of its code base is open source, with only a small percentage truly proprietary and enabling differentiation from its competitors. So why then not distribute and upstream the source to those non-differentiating bits?
Consider rejecting the Compliance Industrial Complex mindset to lower your cost and drastically simplify compliance. Use open source the way it was intended and experience the joy of releasing your source code to benefit your bottom line and the open source ecosystem from which you will continue to reap increasing benefits.
------------------------
作者简介：
[![Picture of Jeffrey Robert Kaufman](https://opensource.com/sites/default/files/styles/profile_pictures/public/pictures/kaufman-picture.jpg?itok=FPIizDR-)][4] Jeffrey Robert Kaufman - Jeffrey R. Kaufman is an Open Source IP Attorney for Red Hat, Inc., the world’s leading provider of open source software solutions. Jeffrey also serves as an adjunct professor at the Thomas Jefferson School of Law. Previous to Red Hat, Jeffrey served as Patent Counsel at Qualcomm Incorporated providing open source counsel to the Office of the Chief Scientist. Jeffrey holds multiple patents in RFID, barcoding, image processing, and printing technologies.[More about me][2]
--------------------------------------------------------------------------------
via: https://opensource.com/article/17/9/economically-efficient-model
作者：[Jeffrey Robert Kaufman ][a]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]:https://opensource.com/users/jkaufman
[1]:https://opensource.com/article/17/9/economically-efficient-model?rate=0SO3DeFAxtgLdmZxE2ZZQyTRTTbu2OOlksFZSUXmjJk
[2]:https://opensource.com/users/jkaufman
[3]:https://opensource.com/user/74461/feed
[4]:https://opensource.com/users/jkaufman
[5]:https://opensource.com/users/jkaufman
[6]:https://opensource.com/users/jkaufman
[7]:https://opensource.com/tags/law
[8]:https://opensource.com/tags/licensing
[9]:https://opensource.com/participate

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sources/tech/20170906 The Incredible Growth of Python.md Normal file
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The Incredible Growth of Python
============================================================
We [recently explored][3] how wealthy countries (those defined as [high-income][4] by the World Bank) tend to visit a different set of technologies than the rest of the world. Among the largest differences we saw was in the programming language Python. When we focus on high-income countries, the growth of Python is even larger than it might appear from tools like [Stack Overflow Trends][5], or in other rankings that consider global software development.
In this post, we’ll explore the extraordinary growth of the Python programming language in the last five years, as seen by Stack Overflow traffic within high-income countries. The term “fastest-growing” can be [hard to define precisely][6], but we make the case that Python has a solid claim to being the fastest-growing major programming language.
All the numbers discussed in this post are for high-income countries; they’re generally representative of trends in the United States, United Kingdom, Germany, Canada, and other such countries, which in combination make up about 64% of Stack Overflow’s traffic. Many other countries such as India, Brazil, Russia, and China also make enormous contributions to the global software development ecosystem, and this post is less descriptive of those economies, though we’ll see that Python has shown growth there as well.
It’s worth emphasizing up front that the number of users of a language isn’t a measure of the language’s quality: we’re  _describing_  the languages developers use, but not prescribing anything. (Full disclosure: I [used to program][7]primarily in Python, though I have since switched entirely to R).
### Python’s growth in high-income countries
You can see on [Stack Overflow Trends][8] that Python has been growing rapidly in the last few years. But for this post we’ll focus on high-income countries, and consider visits to questions rather than questions asked (this tends to give similar results, but has less month-by-month noise, especially for smaller tags).
We have data on Stack Overflow question views going back to late 2011, and in this time period we can consider the growth of Python relative to five other major programming languages. (Note that this is therefore a shorter time scale than the Trends tool, which goes back to 2008). These are currently six of the ten most-visited Stack Overflow tags in high-income countries; the four we didn’t include are CSS, HTML, Android, and JQuery.
![](https://zgab33vy595fw5zq-zippykid.netdna-ssl.com/wp-content/uploads/2017/09/growth_major_languages-1-1024x878.png)
June 2017 was the first month that Python was the most visited tag on Stack Overflow within high-income nations. This included being the most visited tag within the US and the UK, and in the top 2 in almost all other high income nations (next to either Java or JavaScript). This is especially impressive because in 2012, it was less visited than any of the other 5 languages, and has grown by 2.5-fold in that time.
Part of this is because of the seasonal nature of traffic to Java. Since it’s [heavily taught in undergraduate courses][9], Java traffic tends to rise during the fall and spring and drop during the summer. Will it catch up with Python again by the end of the year? We can try forecasting the next two years of growth with a [model called “STL”][10], which combines growth with seasonal trends to make a prediction about future values.
![](https://zgab33vy595fw5zq-zippykid.netdna-ssl.com/wp-content/uploads/2017/09/projections-1-1024x878.png)
According to this model, Python could either stay in the lead or be overtaken by Java in the fall (it’s roughly within the variation of the model’s predictions), but it’s clearly on track to become the most visited tag in 2018\. STL also suggests that JavaScript and Java will remain at similar levels of traffic among high income countries, just as they have for the last two years.
### What tags are growing the fastest overall?
The above was looking only at the six most-visited programming languages. Among other notable technologies, which are currently growing the fastest in high-income countries?
We defined the growth rate in terms of the ratio between 2017 and 2016 share of traffic. We decided to consider only programming languages (like Java and Python) and platforms (such as iOS, Android, Windows and Linux) in this analysis, as opposed to frameworks like [Angular][11] or libraries like [TensorFlow][12] (although many of those showed notable growth that may be examined in a future post).
Because of the challenges in defining “fastest-growing” described in [this comic][13], we compare the growth to the overall average in a [mean-difference plot][14].
![](https://zgab33vy595fw5zq-zippykid.netdna-ssl.com/wp-content/uploads/2017/09/tag_growth_scatter-1-1-1024x896.png)
With a 27% year-over year-growth rate, Python stands alone as a tag that is both large and growing rapidly; the next-largest tag that shows similar growth is R. We see that traffic to most other large tags has stayed pretty steady within high-income countries, with visits to Android, iOS, and PHP decreasing slightly. We previously examined some of the shrinking tags like Objective-C, Perl and Ruby in our [post on the death of Flash][15]). We can also notice that among functional programming languages, Scala is the largest and growing, while F# and Clojure are smaller and shrinking, with Haskell in between and remaining steady.
There’s an important omission from the above chart: traffic to TypeScript questions grew by an impressive 142% in the last year, enough that we left it off to avoid overwhelming the rest of the scale. You can also see that some other smaller languages are growing similarly or faster than Python (like R, Go and Rust), and there are a number of tags like Swift and Scala that are also showing impressive growth. How does their traffic over time compare to Python’s?
![](https://zgab33vy595fw5zq-zippykid.netdna-ssl.com/wp-content/uploads/2017/09/growth_smaller_tags-1-1024x878.png)
The growth of languages like R and Swift is indeed impressive, and TypeScript has shown especially rapid expansion in an even shorter time. Many of these smaller languages grew from getting almost no question traffic to become notable presences in the software ecosystem. But as this graph shows, it’s easier to show rapid growth when a tag started relatively small.
Note that we’re not saying these languages are in any way “competing” with Python. Rather, we’re explaining why we’d treat their growth in a separate category; these were lower-traffic tags to start with. Python is an unusual case for being both one of the most visited tags on Stack Overflow and one of the fastest-growing ones. (Incidentally, it is also accelerating! Its year-over-year growth has become faster each year since 2013).
### Rest of the world
So far in this post we’ve been analyzing the trends in high-income countries. Does Python show a similar growth in the rest of the world, in countries like India, Brazil, Russia and China?
Indeed it does.
![](https://zgab33vy595fw5zq-zippykid.netdna-ssl.com/wp-content/uploads/2017/09/non_high_income_graph-1-1-1024x731.png)
Outside of high-income countries Python is  _still_  the fastest growing major programming language; it simply started at a lower level and the growth began two years later (in 2014 rather than 2012). In fact, the year-over-year growth rate of Python in non-high-income countries is slightly  _higher_  than it is in high-income countries. We don’t examine it here, but R, the [other language whose usage is positively correlated with GDP][16], is growing in these countries as well.
Many of the conclusions in this post about the growth and decline of tags (as opposed to the absolute rankings) in high-income countries hold true for the rest of the world; there’s a 0.979 Spearman correlation between the growth rates in the two segments. In some cases, you can see a “lagging” phenomenon similar to what happened with Python, where a technology was widely adopted within high-income countries a year or two before it expanded in the rest of the world. (This is an interesting phenomenon and may be the subject of a future blog post!)
### Next time
We’re not looking to contribute to any “language war.” The number of users of a language doesn’t imply anything about its quality, and certainly can’t tell you which language is [more appropriate for a particular situation][17]. With that perspective in mind, however, we believe it’s worth understanding what languages make up the developer ecosystem, and how that ecosystem might be changing.
This post demonstrated that Python has shown a surprising growth in the last five years, especially within high-income countries. In our next post, we’ll start to explore the  _“why”_ . We’ll segment the growth by country and by industry, and examine what other technologies tend to be used alongside Python (to estimate, for example, how much of the growth has been due to increased usage of Python for web development versus for data science).
In the meantime, if you work in Python and are looking to take the next step in your career, here are [some companies hiring Python developers right now on Stack Overflow Jobs][18].
--------------------------------------------------------------------------------
via: 网址
作者：[David Robinson][a]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]:https://stackoverflow.blog/authors/drobinson/
[1]:https://stackoverflow.blog/authors/drobinson/
[2]:https://stackoverflow.blog/authors/drobinson/
[3]:https://stackoverflow.blog/2017/08/29/tale-two-industries-programming-languages-differ-wealthy-developing-countries/?utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python
[4]:https://en.wikipedia.org/wiki/World_Bank_high-income_economy
[5]:https://insights.stackoverflow.com/trends?tags=python%2Cjavascript%2Cjava%2Cc%23%2Cphp%2Cc%2B%2B&utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python
[6]:https://xkcd.com/1102/
[7]:https://stackoverflow.com/search?tab=newest&q=user%3a712603%20%5bpython%5d
[8]:https://insights.stackoverflow.com/trends?tags=python%2Cjavascript%2Cjava%2Cc%23%2Cphp%2Cc%2B%2B&utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python
[9]:https://stackoverflow.blog/2017/02/15/how-do-students-use-stack-overflow/
[10]:http://otexts.org/fpp2/sec-6-stl.html
[11]:https://stackoverflow.com/questions/tagged/angular
[12]:https://stackoverflow.com/questions/tagged/tensorflow
[13]:https://xkcd.com/1102/
[14]:https://en.wikipedia.org/wiki/Bland%E2%80%93Altman_plot
[15]:https://stackoverflow.blog/2017/08/01/flash-dead-technologies-might-next/?utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python
[16]:https://stackoverflow.blog/2017/08/29/tale-two-industries-programming-languages-differ-wealthy-developing-countries/?utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python
[17]:https://stackoverflow.blog/2011/08/16/gorilla-vs-shark/?utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python
[18]:https://stackoverflow.com/jobs/developer-jobs-using-python?utm_source=so-owned&utm_medium=blog&utm_campaign=gen-blog&utm_content=blog-link&utm_term=incredible-growth-python