Empirical Analysis of Programming Language Adoption
Download as PPTX, PDF6 likes3,407 views
This document summarizes an empirical study of programming language adoption. The study analyzed data from online surveys of over 13,000 programmers and software projects from GitHub to model how languages grow and are selected. The key findings were: 1) Languages grow niche by niche as they build libraries and user bases in specific application domains. 2) Extrinsic factors like existing libraries, team familiarity, and project needs dominate language selection over intrinsic properties. 3) Programmers can learn a new language well within 6 months, and their working set of languages remains fluid throughout their career as they refresh skills.
![Confession of a Language Salesman
[P. Coburn]
Change Function threshold to adopt:
perceived adoption need
perceived adoption pain
> 1
FP!!!
new language
3](https://image.slidesharecdn.com/oopslatalk2-131101012050-phpapp01/85/Empirical-Analysis-of-Programming-Language-Adoption-3-320.jpg)
![Confession of a Language Salesman
[P. Coburn]
Change Function threshold to adopt:
perceived adoption need
perceived adoption pain
FP!!!
new language
> 1
FP!!
familiar language
5](https://image.slidesharecdn.com/oopslatalk2-131101012050-phpapp01/85/Empirical-Analysis-of-Programming-Language-Adoption-5-320.jpg)
![Our Data Sets
Viral Campaign
[McIver]
2 year long web survey
13,271 respondents
[Patterson &
Fox]
1,142
massive open
online course
(MOOC)
survey
respondents
2 week web survey
1,679 respondents
software
repositorie
s
217,368 projects
7](https://image.slidesharecdn.com/oopslatalk2-131101012050-phpapp01/85/Empirical-Analysis-of-Programming-Language-Adoption-7-320.jpg)
![[PLATEAU 2013]
200K+
Projects (2000-2010)
14](https://image.slidesharecdn.com/oopslatalk2-131101012050-phpapp01/85/Empirical-Analysis-of-Programming-Language-Adoption-14-320.jpg)
Empirical Analysis of Programming Language Adoption
- 1. EMPIRICAL ANALYSIS OF PROGRAMMING LANGUAGE ADOPTION Leo A. Meyerovich, UC Berkeley Ariel S. Rabkin, Princeton October, 2013
- 3. Confession of a Language Salesman [P. Coburn] Change Function threshold to adopt: perceived adoption need perceived adoption pain > 1 FP!!! new language 3
- 4. Confession of a Language Salesman “From now on, my goal in life would be to also drive the denominator down to zero” - Erik Meijer Confessions of a Used Programming Language Salesman 4
- 5. Confession of a Language Salesman [P. Coburn] Change Function threshold to adopt: perceived adoption need perceived adoption pain FP!!! new language > 1 FP!! familiar language 5
- 6. Science? Adoption literature change function is switching costs Data analysis growth decision making acquisition 6
- 7. Our Data Sets Viral Campaign [McIver] 2 year long web survey 13,271 respondents [Patterson & Fox] 1,142 massive open online course (MOOC) survey respondents 2 week web survey 1,679 respondents software repositorie s 217,368 projects 7
- 8. Demographics Age: ~30 Degree: ~BS in CS Employment: ~programmer 8
- 9. How do languages grow? 9
- 10. Ecological model of adoption Use language in a niche Grow libraries and user base Spread language to more niches 10
- 11. Popular Languages CDF (Ohloh data) 100% 90% 80% 70% 60% 50% Cumulativ 40% e css 30% Use html c shell java javascript 20% 10% 0% c++ python make php bat sql rubyc# Half the projects use 5 languages xml Language 11
- 12. Popular Languages CDF (Ohloh data) 100% 90% 80% 70% 60% DSLs dominate 50% Cumulativ 40% e css 30% Use html c shell java javascript 20% 10% 0% c++ python make php bat sql rubyc# Half the projects use 5 languages xml Language 12
- 13. Odds for Most Languages? (PDF) 100.0000% Java for 16% of projects 10.0000% Long Tail! Supports designing for niches and then growing Proportion 1.0000% of Projects for Language 0.1000% Processing for 0.09% of projects 0.0100% 1 10 Language Rank (Decreasing) 100 13
- 15. Popularity Across Niches 60% 40% blogging: 9% Java search: 29% Popularity 20% 0% Project categories (223) 4% 3% Popularity build tools: 1% Scheme 2% 1% 0% Project categories (223) 15
- 16. Popularity Across Niches 60% low dispersion 40% Popularity 20% 0% Project categories (223) 4% 3% high dispersion Popularity 2% 1% 0% Project categories (223) 16
- 17. Dispersion Decreases as Popularity Increases 1 Java 0.1 C# PL/SQL Assembly 0.01 Fortran Prolog 0.001 Scheme VBScript Popularity Languages grow niche by niche 0.0001 5 4 3 2 1 Dispersion across niches (σ / μ) 0 17
- 18. How Do Programmers Pick Languages? 18
- 19. P(L’ | L) p(popular) 75% Shows importance of familiarity p(repeat) 30% 19
- 20. How Do Languages Get Picked? Development speed? Performance? strongly disagree neutral strongly agree 20
- 21. Relative Importance of Language Aspects (MedStrong) 0% Open source libraries Group legacy Project legacy Self familiarity Team familiarity Target platform Performance Tooling Development speed Hiring Individual feature(s) Correctness Simplicity Commercial libraries 10% 20% 30% 40% 50% 60% 70% 80% Extrinsic niche-specific factors dominate! Intrinsics: performance, correctness, … Be Positive: Design Guides & Opportunities Slashdot survey, Companies with 1-19 employees 21
- 22. Learning: Shelf Life of a Programmer? “Baby Boomers and Gen Xers tend to know C# and SQL. Gen Y knows Python… and Hadoop” Recruiter 22
- 23. Language Users are Age-Invariant
- 24. Mean # Langs. known Languages are learned and forgotten Programmers have a working set that they refresh! 8 6 4 2 know slightly know well 0 20 30 40 Age 50 60
- 25. Median reported time required to “learn a language well” Time to learn is short compared to career 25
- 26. Probability of Knowing a Language All CS Major Not CS Major Taught in school Not Taught in school Functional Scheme, ML, ... 22% 24% 19% 40% 15% Assembly MIPS, … 14% 14% 14% 20% 10% Mathematic al 11% 10% 11% 31% 7% Matlab, R, CS degree unimportantbut coursework matters … 26
- 27. Conclusions Extrinsics dominate: Libraries and familiarity! Model: Niche-by-niche growth Intrinsics secondary: Performance, semantics, IDEs Fluidity = Hope: Programmers know few languages but can refresh within 6 months. 27
- 28. Looking Ahead Language Sociology Programming is done by groups; big knowledge gaps Streamline Empiricism Surveys, experiments (mining already active) Exploit MOOCs! Social Language Design Improve sharing and utilize networks 28
Editor's Notes
- #8: David McIver
- #11: http://www.dreamstime.com/royalty-free-stock-image-small-plant-breaking-rock-image13902286http://jessgibbsphotography.com/wp-content/uploads/2010/05/bright_green_flowering_plants_grow_on_rocks_along_foreshore.jpg
- #23: http://www.theaustralian.com.au/technology/legacy-languages-prove-lucractive-for-dying-breed-of-programmers/story-e6frgakx-1225993874788http://bits.blogs.nytimes.com/2013/07/05/technology-workers-are-young-really-young/