USING STRUCTURAL HOLES METRICS FROM COMMUNICATION NETWORKS TO PREDICT CHANGE DEPENDENCIES
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Transcript of USING STRUCTURAL HOLES METRICS FROM COMMUNICATION NETWORKS TO PREDICT CHANGE DEPENDENCIES
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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
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MOTIVATION SCENARIO
Subsystem A
Class aClass c
Class b
Subsystem B
Class d
Class e
Software systems are composed byArtifacts that
dependes one eachother
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MOTIVATION SCENARIO
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MOTIVATION SCENARIO
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MOTIVATION SCENARIO
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CHANGE DEPENDENCIES
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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
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CONWAY´S LAW
Subsystem A
Class aClass 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”
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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
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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
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RQ1. Can SHM from communication
predict change dependencies?
RQ2. What is the role of SHM from
communication to predict change
dependencies?
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DATA COLLECTION
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Two years of data, Split into six-months timeframes.
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COMPUTING CHANGE DEPENDENCIES
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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)
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BUILDING THE COMMUNICATION
NETWORK
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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
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COMPUTING STRUCTURAL HOLES
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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
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CLASSIFICATION APPROACH
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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
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CLASSIFICATION APPROACH
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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
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RESULTS: RQ1 CAN SHM FROM COMMUNICATION NETWORKS
PREDICT CHANGE DEPENDENCIES?
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RESULTS: RQ1 CAN SHM FROM COMMUNICATION NETWORKS
PREDICT CHANGE DEPENDENCIES?
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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
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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)
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RESULTS: WHAT IS THE ROLE OF SHM FROM COMMUNICATION
TO PREDICT CO-CHANGES?
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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.)