Our paper presentation on CRIWG 14.

1. USING STRUCTURAL HOLES METRICS
FROM COMMUNICATION NETWORKS
TO PREDICT CHANGE DEPENDENCIES
Igor Wiese, Rodrigo Kuroda, Douglas Nassif, Reginaldo Ré,
Gustavo Oliva and Marco Aurélio Gerosa

2. 2
MOTIVATION SCENARIO
Subsystem A
Class a
Class c
Class b
Subsystem B
Class d
Class e
Software systems are
composed by
Artifacts that
dependes one each
other

3. 3
MOTIVATION SCENARIO

4. 4
MOTIVATION SCENARIO

5. 5
MOTIVATION SCENARIO

6. CHANGE DEPENDENCIES
6
Artifacts a1
Artifacts a2
time
Change coupling
commit
A change dependency indicates that two
artifacts changed together (co-changed)
in the past, making them evolutionarily
connected

7. 7
CONWAY´S LAW
Subsystem A
Class a
Class c
Class b
Subsystem B
Class d
Class e
"organizations
which design
systems are
constrained to
produce designs
which are copies
of the
communication
structures of
these
organization”

8. 8
CONWAY´S LAW
Artifacts a1
Artifacts a2
time
Change coupling
commit
to check the impact of the structure of communications networks to predict
the occurrence of change dependencies among artifacts

9. 9
STRUCTURAL HOLE
Structural Holes Metric (SHM) can reflect gaps
between nodes in a social network indicating
that a developer on either side of the hole have
access to different flows of information.
Network A Network B

10. 10
RQ1. Can SHM from communication
predict change dependencies?
RQ2. What is the role of SHM from
communication to predict change
dependencies?

11. DATA COLLECTION
11
Two years of data, Split into six-months timeframes.

12. COMPUTING CHANGE DEPENDENCIES
12
Combine all files
in pairs
Going throught
the timeframe
analysis
Filter out co-
changes (5 couplings
as support count)
Split the classes
(higher than the median =
strong)

13. BUILDING THE COMMUNICATION
NETWORK
13
Recover the PR
for each change
dependency
Build the network
Calculate the
SHM metrics
d1
d2
Submitted Pull Request (PR)
First comment
d3 Second comment

14. COMPUTING STRUCTURAL HOLES
14
Recover the PR
for each change
dependency
Build the network
Calculate the
SHM metrics
• Effective Size is the portion of non-redundant
neighbors of a node
• Efficiency normalizes the effective size by the
number of neighbors
• Constraint measure the lack of holes among
neighbors
• Hierarchy measures the concentration of
constraint to a single node
Few values: non-redundant neighbors
Few values: high redundant neighbors
High values: many alternatives to access
the neighbors
High values: constraint is concentrated in
a single neighbor

15. CLASSIFICATION APPROACH
15
𝐹 − measure = 2 ∗
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
A good model yields both high precision and
high recall. However, increasing one often
reduces the other.
F-measure returns a balanced
score of recall and precision

16. CLASSIFICATION APPROACH
16
𝑀𝐶𝐶 =
𝑅𝑒𝑐𝑎𝑙𝑙 𝑊𝑒𝑎𝑘𝐶𝑜𝐶ℎ𝑎𝑛𝑔𝑒 + 𝑅𝑒𝑐𝑎𝑙𝑙 𝑆𝑡𝑟𝑜𝑛𝑔𝐶𝑜𝐶ℎ𝑎𝑛𝑔𝑒 − 1 ∗
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑊𝑒𝑎𝑘𝐶𝑜𝐶ℎ𝑎𝑛𝑔𝑒 + 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑆𝑡𝑟𝑜𝑛𝑔𝐶𝑜𝐶ℎ𝑎𝑛𝑔𝑒 − 1
MCC returns a value between -1 and +1. A
coefficient of +1 represents a perfect
prediction, 0 means a random prediction and
-1 indicates total disagreement between
prediction and observations

17. RESULTS: RQ1 CAN SHM FROM COMMUNICATION NETWORKS
PREDICT CHANGE DEPENDENCIES?
17

18. RESULTS: RQ1 CAN SHM FROM COMMUNICATION NETWORKS
PREDICT CHANGE DEPENDENCIES?
18
Models controlled by
process metrics (number
of commits and developers)
Area under the
Curve > 0.7
1
2
cannot found statistical
difference between SHM
and Process metrics
3

19. RESULTS: RQ2 WHICH IS THE ROLE OF SHM FROM
COMMUNICATION TO PREDICT CO-CHANGES?
• We generated the tree using the Classication tree algorithm.
• To rank the metrics we counted the amount of times that each metric
appeared on the first four levels of each tree for both projects.
• We generated 8 treens (4 timeframes periods for each Project)
• Node.JS only with SHM
• constraintAVG (6), hierarchySUM (6), hierarchyAVG (3), efficiencySUM (3),
• Node.Js with process
• Commits (19), updates (14), effectiveSizeSUM (6), efficiencySUM (2),
constraintMAX (3)
• For Rails with process
• commits (8), updates (3), efficiencyAVG (3), effectiveSizeSUM (3),
effectiveSizeAVG (2), constraintSUM (2), efficiencySUM (2)

20. RESULTS: WHAT IS THE ROLE OF SHM FROM COMMUNICATION
TO PREDICT CO-CHANGES?

21. CONTRIBUTIONS
• As main contribution, we shown that SHM obtained from
communication networks can support the Conway’s law and predict
change dependencies.
• Since this first effort to use social metrics presented promising results,
we will explore more broadly this social dimension of development
process.
• Adding new projects
• Adding new social metrics
• Comparing with other software aspects (code metrics,
architectural aspects, etc.)

22. http://lapessc.ime.usp.br/
Thank you
igor@utfpr.edu.br