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.

1. EMPIRICAL
ANALYSIS
OF
PROGRAMMING
LANGUAGE
ADOPTION
Leo A. Meyerovich, UC
Berkeley
Ariel S. Rabkin, Princeton
October, 2013

2. Why Adoption?
2

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

14. [PLATEAU 2013]
200K+
Projects (2000-2010)
14

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

29. Socio-PLT
www.eecs.berkeley.edu/~lmeyerov
29