Software engineering is inherently a collaborative venture, involving many stakeholders that coordinate their efforts to produce large software systems. While importance of human aspects in software engineering has been recognised already in the 1970s, emergence of open source software (late 1990s) and platforms such as Stack Overflow and GitHub (late 2000s) enabled application of empirical methods to study of human aspects of software engineering. In the first part of the talk we present a selection of recent results pertaining to two main questions: who are the software developers and in what kind of activities they engage. The second part of the talk focuses on tools and techniques that have been used to obtain the aforementioned results.

1. Human
Aspects
of
So0ware
Engineering
Alexander
Serebrenik
Eindhoven
University
of
Technology,
NL

3. 1970s
…
1998
2008
2014
SocialCom,
SocInfo,
SBP,
CHASE
MSR
books,
conferences

4. MS
Windows
post-­‐release
fault
predic5on
Precision
predicted
&
correct
/
predicted
Recall
predicted
&
correct
/
correct
Code
churn
78.6%
79.9%
Code
complexity
79.3%
66.0%
Code
coverage
83.8%
54.5%
Code
dependencies
74.4%
69.9%
Organiza5onal
structure
86.2%
84.0%
Socio-­‐technical
network
76.9%
70.5%

8. >
90%
in
WordPress
&
Drupal
>
95%
in
FLOSS
surveys
>
87%
in
GNOME
>
70%
in
soBware-­‐related
jobs
(NSF)
MEN

9. FLOSS
2013
YOUNG(?)
median
Is
Programming
Knowledge
Related
to
Age?
An
Explora8on
of
Stack
Overflow.
Morrison,
P.,
Murphy-­‐Hill,
E.
MSR
2013.
FLOSS
2013:
A
survey
dataset
about
free
soPware
contributors:
challenges
for
cura8ng,
sharing
and
combining.
Robles,
G.,
Arjona-­‐Reina,
L.,
Vasilescu,
B.,
Serebrenik,
A.,
Gonzalez-­‐Barahona,
J.M.
MSR
2014

10. FLOSS
2013

11. FLOSS
2013

12. FLOSS
2013
Europe,US,CA,AU
Brazil/Argen5na

15. PAGE 1508/07/14
.cpp .po
.jpg
/test/
/library/ .doc
makefile .sql .conf

16. Occasional
contributors
Frequent
contributors
On the variation and specialization of workload---A case
study of the Gnome ecosystem community Vasilescu, B.,
Serebrenik, A., Goeminne, M., Mens, T. Empirical Sw Engg

17. For
which
of
those
ac[vi[es
would
Joe
Average
be
more
ac[ve
than
Jane
Average?
For
which
ac[vi[es
no
differences
would
be
observed?

18. PAGE 1808/07/14
.cpp .po
.jpg
/test/
/library/ .doc
makefile .sql .conf

19. PAGE 1908/07/14
.cpp .po
.jpg
/test/
/library/ .doc
makefile .sql .conf

20. PAGE 2008/07/14
.cpp .po
.jpg
/test/
/library/ .doc
makefile .sql .conf
No differences
in preferences
for activities,
differences in
the amount of
commits

21. sample
Engage
for longer
Ask more
questions
No diff in #answers
Women can
contribute to SO
but choose not to!
Gender, representation and online participation: A quantitative study,
Vasilescu, B., Capiluppi, A., and Serebrenik, A., Interacting with Computers

22. sample
No significant
differences in
#questions, #answers,
length of engagement
Disengagement of
women is activity/
platform specific!

24. • More
ac[ve
commi_ers
ask
more
on
SO
• More
ac[ve
askers
commit
more
on
GitHub
StackOverflow
and
GitHub:
Associa8ons
between
soPware
development
and
crowdsourced
knowledge,
Vasilescu,
B.,
Filkov,
V.
and
Serebrenik,
A.,
In
Social
Compu8ng,
2013,
IEEE.

25. StackOverflow
and
GitHub:
Associa8ons
between
soPware
development
and
crowdsourced
knowledge,
Vasilescu,
B.,
Filkov,
V.
and
Serebrenik,
A.,
In
Social
Compu8ng,
2013,
IEEE.

26. • Contributors
to
both
communi[es
(more
likely
developers
than
non-­‐developers)
are
more
ac[ve
than
those
who
focus
on
just
one.
How
Social
Q&A
sites
are
changing
knowledge
sharing
in
open
source
soPware
communi8es,
Vasilescu,
B.,
Serebrenik,
A.,
Devanbu,
P.T.
and
Filkov,
V.,
In
CSCW
2014,
ACM.
help

27. How
Social
Q&A
sites
are
changing
knowledge
sharing
in
open
source
soPware
communi8es,
Vasilescu,
B.,
Serebrenik,
A.,
Devanbu,
P.T.
and
Filkov,
V.,
In
CSCW
2014,
ACM.
help

29. Tools
and
Techniques

30. PAGE 3008/07/14
Euphegenia Doubtfire,
euphegenia@hotmail.com
Robin Williams,
robinw@gmail.com

31. PAGE 3108/07/14
Ordering Rajesh Sola Sola Rajesh
Spelling: misspelling,
diacritics, punctuation
Rene Engelhard Fene Engelhard
Démurget Demurget
J. A. M. Carneiro J A M Carneiro
Middle initials,
patronyms, nicknames,
additional surnames,
incomplete names
Daniel M. Mueth Daniel Mueth
Alexander Alexandrov
Shopov
Alexander Shopov
Carlos Garnacho Parro Carlos Garnacho
Jacob “Ulysses”
Berkman
Jacob Berkman
A S Alam Amanpreet Singh Alam
Name variants:
transliteration,
diminutives
Γιωργοσ Georgios
Mike Gratton Michael Gratton
Software-specific:
usernames, projects,
tooling artefacts
mrhappypants Aaron Brown
Arturo Tena/libole2 Arturo Tena
(16:06) Alex Roberts Alex Roberts
Mix Any combination of those

32. Jane
Smith
Jane
Alice
Smith,
jsmith@...
J.
Smith,
jane.smith@...
Jane
Smith,
jane@...
iden[ty
merging/
iden[ty
reconcilia[on/
developer
matching

33. <Jane,
jsmith@gmail.com>
<Jane
Smith,
jsmith@gmail.com>
Ø iden[cal
mail
addresses
è
the
same
person
Ø also
useful
for
MD5
hashes
in
Stack
Overflow
<Jane
Smith,
jsmith@gmail.com>
<Jane
Smith,
jsmith@yahoo.com>
Ø likely
to
be
the
same
person
but
might
give
false
posi[ves
if
these
are
different
Janes…

34. Latent
Seman[c
Analysis
1
..
..
..
1
..
..
..
1
..
..
..
?
..
..
..
1
..
..
..
john
johnd
joseph
jdoe
doe
johnd@domainA:
{john,
johnd,
joseph,
doe}
<John
Doe,
johnd@domainA>
<John
Joseph
Doe,
johnd@domainA>
Document-­‐term
matrix

35. Latent
Seman[c
Analysis
1
..
..
..
1
..
..
..
1
..
..
..
3/4
..
..
..
1
..
..
..
john
johnd
joseph
jdoe
doe
johnd@domainA:
{john,
johnd,
joseph,
doe}
<John
Doe,
johnd@domainA>
<John
Joseph
Doe,
johnd@domainA>
Document-­‐term
matrix
max
similarity(jdoe,
{john,
johnd,
joseph,
doe})
=
similarity(jdoe,
doe)
=
1
–
Levenshtein(jdoe,
doe)
/
max(
length(jdoe),
length(doe))
=
1
–
1/4
=
3/4

36. Latent
Seman[c
Analysis
1
..
..
..
1
..
..
..
1
..
..
..
3/4
..
..
..
1
..
..
..
john
johnd
joseph
jdoe
doe
Inverse
document
frequency
(Singular
value
decomposi[on)
Rank
(noise)
reduc[on
Cosine
between
documents
Merge
similar
documents

37. h_p://uberpython.wordpress.com/2012/01/01/precision-­‐recall-­‐sensi[vity-­‐and-­‐specificity/
Which
technique
is
be_er?

38. h_p://uberpython.wordpress.com/2012/01/01/precision-­‐recall-­‐sensi[vity-­‐and-­‐specificity/

39. Most
contributors
have
only
one
“iden[ty”:
all
techniques
are
good
enough
for
the
average
case.
Advanced
techniques
are
be_er
in
presence
of
noise.
Choose
your
weapons
wisely!

40. /
SET
/
W&I
PAGE
40
08/07/14
What
is
your
gender?

44. Name
+
Loca[on
=
Gender

45. Lonzo
⇒
Alonzo
w35l3y
⇒
wesley
Name
+
Loca[on
=
Gender

46. <[tle>Ben
Kamens</[tle>
…
<h1>We&#8217;re
willing
to
be
embarrassed
about
what
we
<em>haven&#8217;t</
em>
done&#8230;</h1>
Heuris8cs:
[tle
+
first
h1
Ben
Kamens
We’re
willing
to
be
embarrassed
about
what
we
haven’t
done…
<PERSON>Ben
Kamens</PERSON>
We’re
willing
to
be
embarrassed
about
what
we
haven’t
done…
Stanford
Named
En8ty
Tagger

47. Quality
of
gender
resolu[on:
Survey
Self-­‐
iden[fica[on
As
inferred
Total
M
F
?
M
60
3
43
106
F
2
5
4
11
Self-­‐
iden[fica[on
As
inferred
Total
M
F
?
M
90
3
13
106
F
2
9
0
11
+
avatars,
other
social
media
sites
(manually)

50. SANER
2015
22nd
Interna[onal
Conference
on
So0ware
Analysis,
Evolu[on
and
Reengineering
• Abstracts:
November
7,
2015
• Papers:
November
14,
2015