in Open Source Projects? Gerardo Massimiliano Rocco - - PowerPoint PPT Presentation
Who is going to Mentor Newcomers in Open Source Projects? Gerardo Massimiliano Rocco Sebastiano Canfora Di Penta Oliveto Panichella C ontext and M otivations Software Development
Gerardo Massimiliano Rocco Sebastiano Canfora Di Penta Oliveto Panichella
Context and Motivations
How?
Case Study
With a GOOD TRAINING
Can immediately start to work ACTIVELY
Newcomer
Zhou and Mockus Better training from Senior Developers
Newcomer
Low Sociability
Dagenais et al. MENTOR
Newcomer Mentoring of project newcomers is highly desirable
SVN
GIT CVS
Small Projects: find Mentors is a trivial problem Large Projects: : find Mentors is not a trivial problem
.........
(Young and newcOmer Developer Assistant)
Approach for Mentors Identification in Open Source Projects
SVN GIT CVS
?
What factor can be used to identify mentors?
What factor can be used to identify mentors?
How does Arnetminer works?
f1: they published Many papers Together f2: advisor published More than the Student f3: advisor older than the student f4: student published her first paper(s) with the advisor
Ranks pairs of researchers according to four factors:
Time
F1: Exchanged emails
When Alice joins the project
F1: Exchanged emails
Time
F2: overall amount of emails
F2: overall amount of emails
F2: overall amount of emails
F3: project age
Time
F3: project age
F4: newcomer early emails
Time
First emails by Alice in the project
F4: newcomer early emails
F5: Commits
When Alice joins the project
Time
F5: Commits
What factors can be used to identify mentors?
5 1 i i i f
Time
Time
Time
t
Time
t
Mentor with adequate skills
Time
Inspired to the work On Bug Triaging by J. Anvik et al. 2011
Time
Inspired to the work On Bug Triaging by J. Anvik et al. 2011
t
Time
Inspired to the work On Bug Triaging by J. Anvik et al. 2011
t
Time
Inspired to the work On Bug Triaging by J. Anvik et al. 2011
t
Time
Inspired to the work On Bug Triaging by J. Anvik et al. 2011
t
Time
Inspired to the work On Bug Triaging by J. Anvik et al. 2011
t
DICE SIMILARITY
systems
identify mentors
projects
software
Apache FreeBSD PostgreSQL Python Samba
Period (Training set)
08/2001-03/2002 11/1998-02/2000 10/1998-05/2001 05/2000-05/2001 04/1998-09/2000
Period (Test set)
04/2002-12/2008 03/2000-10/2008 06/2001-03/2008 06/2001-12/2008 10/2000-12/2008
# of Mentors (Training set)
19 65 10 28 17
# of Newcomers (Training set)
13 33 8 32 33
# of Newcomers (Test set)
13 33 7 31 33
Training and Test sets for evaluating Yoda.
?
RQ1: How can we identify mentors from the past
history of a software project?
SCORE 2.5 1.5 1.5 1.5 1.5 1.5 ………. COUPLES ……….
5 1 i i i f
RQ1: How can we identify mentors from the past
history of a software project?
SCORE 2.5 1.5 1.5 1.5 1.5 1.5 ………. COUPLES ……….
5 1 i i i f
Manual Validation
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 18 19 20 21 22 23 24
Precision Number of newcomer‐mentor pairs Possible Configurations
f1
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 18 19 20 21 22 23 24
Precision Number of newcomer‐mentor pairs Possible Configurations
f1 +f2+ f3
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 18 19 20 21 22 23 24
Precision Number of newcomer‐mentor pairs Possible Configurations
f1 +f2+ f4
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 18 19 20 21 22 23 24
Precision Number of newcomer‐mentor pairs Possible Configurations
f5
(Baseline)
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 18 19 20 21 22 23 24
Precision Number of newcomer‐mentor pairs
Apache
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 12 14 16 18 20 22
Precision Number of newcomer‐mentor pairs
PostgreSQL
f1 f1 +f2+ f3 f1 +f2+ f4 f5
(Baseline)
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 18 19 20 21 22 23 24
Precision Number of newcomer‐mentor pairs
Apache
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 12 14 16 18 20 22
Precision Number of newcomer‐mentor pairs
PostgreSQL
f1 f1 +f2+ f3 f1 +f2+ f4 f5
(Baseline)
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 23 25 27 29 31 33 35 37 39 41
Precision Number of newcomer‐mentor pairs
FreeBSD
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 24 26 28 30 32 34 36 38 40 42 44 46 48
Precision Number of newcomer‐mentor pairs
Python
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
30 32 34 36 38 40 42
Precision Number of newcomer‐mentor pairs
Samba
RQ1: How can we identify mentors from the past
history of a software project?
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 23 25 27 29 31 33 35 37 39 41
Precision Number of newcomer‐mentor pairs
FreeBSD
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 24 26 28 30 32 34 36 38 40 42 44 46 48
Precision Number of newcomer‐mentor pairs
Python
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
30 32 34 36 38 40 42
Precision Number of newcomer‐mentor pairs
Samba
USEFUL FACTORS FOR MENTORS IDENTIFICATION
0.5*f1 + 0.25*f2 + 0.25*f4 0.5*f1 + 0.25*f2 + 0.25*f3 f1
RQ2: To what extent would it be possible to
recommend mentors to newcomers joining a software project?
RQ2: To what extent would it be possible to
recommend mentors to newcomers joining a software project?
RQ2: To what extent would it be possible to
recommend mentors to newcomers joining a software project?
YODA make it is possible possible to recommend Mentors
Not all Committers Are Good Mentors
Questions Asked:
FreeBSD Postgre- SQL Python Apache Samba
37
37
15
23
23
FreeBSD Postgre- SQL Python Apache Samba
92% 58% 8% 42%
0% 20% 40% 60% 80% 100% Did mentoring? Had a mentor? YES NO
92% 58% 8% 42%
0% 20% 40% 60% 80% 100% Did mentoring? Had a mentor? YES NO
Yes, I received
mentor was… Yes, I did mentoring…
18% 36% 45% 0% 0% 33% 56% 11% 0% 0%
0% 20% 40% 60% Very important Important Neutral Not important Useless at all Effect of mentor Effect on newcomer
18% 36% 45% 0% 0% 33% 56% 11% 0% 0%
0% 20% 40% 60% Very important Important Neutral Not important Useless at all Effect of mentor Effect on newcomer
18% 36% 45% 0% 0% 33% 56% 11% 0% 0%
0% 20% 40% 60% Very important Important Neutral Not important Useless at all Effect of mentor Effect on newcomer
18% 36% 45% 0% 0% 33% 56% 11% 0% 0%
0% 20% 40% 60% Very important Important Neutral Not important Useless at all Effect of mentor Effect on newcomer
Is very important that mentor share knowledge with a mentee…
19% 42% 38% 0% 0% 10% 20% 30% 40% 50% Experience Communication skills Project knowledge Others
19% 42% 38% 0% 0% 10% 20% 30% 40% 50% Experience Communication skills Project knowledge Others
19% 42% 38% 0% 0% 10% 20% 30% 40% 50% Experience Communication skills Project knowledge Others
My first Mentor had a very strong and technical background