This document presents YODA, an approach for identifying mentors in open source software projects. It aims to address the challenge of finding suitable mentors for newcomers joining large projects. YODA analyzes project data like mailing lists and version control systems to identify factors that characterize past mentors. It then uses these factors to recommend potential mentors for new joiners. An empirical study on five projects found communication-related factors like email exchanges are useful for identifying mentors. A survey of developers indicated mentoring is important and that technical skills and communication ability are key qualities of an effective mentor. Future work could focus on improving YODA's recommendation capabilities.

1. Who is going to Mentor Newcomers
in Open Source Projects?
Gerardo
Canfora
Massimiliano
Di Penta
Rocco
Oliveto
Sebastiano
Panichella

2. Context and Motivations
• Software Development
How?
• Training via Mentoring
Case Study
•Explorative analysis
•Recommendation system evaluation

3. Training Project Newcomers
With a GOOD
TRAINING
Can immediately
start to work
ACTIVELY
Newcomer

4. Previous Work...
Newcomer
Low
Sociability
Better
training from
Senior Developers
Zhou and Mockus

5. Previous Work...
Newcomer
MENTOR
Mentoring of
project newcomers
is highly desirable
Dagenais et al.

6. Characteristics of a good Mentor

7. Sources of Information
SVN
GIT CVS

8. Mentoring Small/large Projects
Small Projects: find Mentors
is a trivial problem
Large Projects: : find Mentors
is not a trivial problem
.........

9. YODA
(Young and newcOmer Developer Assistant)
Approach for
Mentors Identification
in Open Source Projects

10. YODA: two pashes
What factor can be
used to identify
mentors?
SVN
GIT CVS
?

11. RQ1: Identify past mentors
What factor can be used
to identify mentors?

12. How does Arnetminer works?
Ranks pairs of researchers according to four factors:
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

13. Heuristics to identify Mentors
F1: Exchanged emails
Time

14. Heuristics to identify Mentors
F1: Exchanged emails
Time
When Alice joins
the project

15. Heuristics to identify Mentors
F2: overall amount of emails

16. Heuristics to identify Mentors
F2: overall amount of emails

17. Heuristics to identify Mentors
F2: overall amount of emails

18. Heuristics to identify Mentors
F3: project age

19. Heuristics to identify Mentors
F3: project age
Time

20. Heuristics to identify Mentors
F4: newcomer early emails

21. Heuristics to identify Mentors
F4: newcomer early emails
Time
First emails by Alice in the
project

22. Heuristics to identify Mentors
F5: Commits

23. Heuristics to identify Mentors
F5: Commits
When Alice joins
the project
Time

24. Aggregating the factors
What factors can be used
to identify mentors?
5
w f
i 1
i i

25. Recommend Mentors
Time

26. Recommend Mentors
Time

27. Recommend Mentors
Time
t

28. Recommend Mentors
Mentor with
adequate skills
Time
t

29. Recommend Mentors
Inspired to the work
On Bug Triaging
by J. Anvik et al.
2011
Time

30. Recommend Mentors
Inspired to the work
On Bug Triaging
by J. Anvik et al.
2011
Time
t

31. Recommend Mentors
Inspired to the work
On Bug Triaging
by J. Anvik et al.
2011
Time
t

32. Recommend Mentors
Inspired to the work
On Bug Triaging
by J. Anvik et al.
2011
Time
t

33. Recommend Mentors
Inspired to the work
On Bug Triaging
by J. Anvik et al.
2011
Time
t

34. Recommend Mentors
Inspired to the work
On Bug Triaging
by J. Anvik et al.
2011
Time
t
DICE
SIMILARITY

35. Empirical Study
• Goal: analyze data from mailing lists and versioning
systems
• Purpose: investigating which factors can be used to
identify mentors
• Quality focus: recommend mentors in software
projects
• Context: mailing lists and versioning systems of five
software
 Apache, FreeBSD, PostgreSQL, Python and Samba

36. Context
Training and Test sets for evaluating Yoda.
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

37. Research Questions
?

38. RQ1: How can we identify mentors from the past
history of a software project?
COUPLES
SCORE
5
2.5
1.5
1.5
1.5
1.5
1.5
……….
……….
w f
i 1
i i

39. RQ1: How can we identify mentors from the past
history of a software project?
COUPLES
SCORE
5
w f
2.5
i 1
1.5
1.5
1.5
1.5
1.5
……….
……….
Manual
Validation
i i

40. RQ1: How can we identify mentors from the past
history of a software project?
Possible
Configurations
100%
90%
f1
80%
Precision
70%
60%
50%
40%
30%
20%
10%
0%
18
19
20
21
22
23
Number of newcomer‐mentor pairs
24

41. RQ1: How can we identify mentors from the past
history of a software project?
Possible
Configurations
100%
90%
f1 +f2+ f3
80%
Precision
70%
60%
50%
40%
30%
20%
10%
0%
18
19
20
21
22
23
Number of newcomer‐mentor pairs
24

42. RQ1: How can we identify mentors from the past
history of a software project?
Possible
Configurations
100%
90%
f1 +f2+ f4
80%
Precision
70%
60%
50%
40%
30%
20%
10%
0%
18
19
20
21
22
23
Number of newcomer‐mentor pairs
24

43. RQ1: How can we identify mentors from the past
history of a software project?
Possible
Configurations
100%
90%
f5
80%
Precision
70%
60%
50%
40%
30%
20%
10%
0%
18
19
20
21
22
23
Number of newcomer‐mentor pairs
24
(Baseline)

44. RQ1: How can we identify mentors from the past
Apache
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Precision
Precision
history of a software project?
18
19
20
21
22
23
24
Number of newcomer‐mentor pairs
f1
f1 +f2+ f3
PostgreSQL
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
12
14
16
18
20
22
Number of newcomer‐mentor pairs
f1 +f2+ f4
f5(Baseline)

45. RQ1: How can we identify mentors from the past
Apache
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Precision
Precision
history of a software project?
18
19
20
21
22
23
24
Number of newcomer‐mentor pairs
f1
f1 +f2+ f3
PostgreSQL
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
12
14
16
18
20
22
Number of newcomer‐mentor pairs
f1 +f2+ f4
f5(Baseline)

46. RQ1: How can we identify mentors from the past
history of a software project?
Precision
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
FreeBSD
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
23
24 26 28 30 32 34 36 38 40 42 44 46 48
25
Samba
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
30
27
29
31
33
35
37
39
Number of newcomer‐mentor pairs
Number of newcomer‐mentor pairs
Precision
Precision
Python
32
34
36
38
40
42
Number of newcomer‐mentor pairs
41

47. RQ1: How can we identify mentors from the past
history of a software project?
Precision
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
USEFUL FACTORS FOR
MENTORS IDENTIFICATION
FreeBSD
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
23
24 26 28 30 32 34 36 38 40 42 44 46 48
Number of newcomer‐mentor pairs
f1
25
Samba
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
30
27
29
31
33
35
37
39
Number of newcomer‐mentor pairs
0.5*f1 + 0.25*f2 + 0.25*f3
0.5*f1 + 0.25*f2 + 0.25*f4
Precision
Precision
Python
32
34
36
38
40
42
Number of newcomer‐mentor pairs
41

48. RQ2: To what extent would it be possible to
recommend mentors to newcomers joining a
software project?

49. RQ2: To what extent would it be possible to
recommend mentors to newcomers joining a
software project?

50. 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

51. Why don’t just using
Top Committers?

52. Why don’t just using
Top Committers?

53. Why don’t just using
Top Committers?
Not all Committers Are
Good Mentors

54. Surveying Projects Developers
Questions Asked:
- Done/received mentoring
- Perceived importance of mentoring
- What makes a good Mentor

55. Sent to 114 Subjects…
Samba
.....
37
FreeBSD
.....
37
PostgreSQL
Python
.....
15
.....
23
Apache
.....
23

56. Obtained Answare…
Samba
FreeBSD
PostgreSQL
Python
Apache
-

57. Done/received mentoring?
Had a mentor?
58%
Did mentoring?
42%
92%
0%
8%
20% 40% 60% 80% 100%
YES NO

58. Done/received mentoring?
Had a mentor?
58%
42%
Yes, I received
Mentoring. My
mentor was…
Yes, I did
mentoring…
>
Did mentoring?
0%
92%
8%
20% 40% 60% 80% 100%
YES NO

59. Perceived importance of mentoring
Useless at all
0%
0%
Not important
0%
0%
Neutral
11%
45%
56%
Important
Very important
0%
Effect of mentor
36%
33%
18%
20%
40%
60%
Effect on newcomer

60. Perceived importance of mentoring
Useless at all
0%
0%
Not important
0%
0%
Neutral
11%
45%
56%
Important
Very important
0%
Effect of mentor
36%
33%
18%
20%
40%
60%
Effect on newcomer

61. Perceived importance of mentoring
Useless at all
0%
0%
Not important
0%
0%
Neutral
11%
45%
56%
Important
Very important
0%
Effect of mentor
36%
33%
18%
20%
40%
60%
Effect on newcomer

62. Perceived importance of mentoring
Useless at all
0%
0%
Not important
0%
0%
11%
Is very important that
Neutral
mentor share knowledge
with a mentee…
Important
Very important
0%
Effect of mentor
45%
56%
36%
33%
18%
20%
40%
60%
Effect on newcomer

63. What makes a good Mentor
Others
0%
Project knowledge
38%
Communication skills
Experience
42%
19%
0% 10% 20% 30% 40% 50%

64. What makes a good Mentor
Others
0%
Project knowledge
38%
Communication skills
Experience
42%
19%
0% 10% 20% 30% 40% 50%

65. What makes a good Mentor
Others
0%
My knowledge
Project first Mentor
38%
had a very strong and
technical background
Communication skills
Experience
42%
19%
0% 10% 20% 30% 40% 50%

66. Conclusion

67. Conclusion

68. Conclusion

69. Conclusion

70. Conclusion

71. Future Work