SEKE 2012 Proceedings - Knowledge Systems Institute

Our approach checks whether there is one of the following
dependence relations in SDG of Notify candidate: (1) Notify
method creates an object, and a parameter of Update depends
on the object; (2) some fields of Observer depend on fields of
Subject.
B. Case Study
Fig. 3 shows an Observer instance identified by JDP-
Detector from open source software JEdit 4.3.2. In the instance,
HelpHistoryModel and HelpHistoryModelListener act as Subject
and Observer, respectively, and fireUpdate is the candidate
of Notify method. JDP-Detector finds that there are dependence
paths between fields of HelpHistoryModel and HelpHistory-
ModelListener in SDG of fireUpdate. This indicates that
fireUpdate acts as Notify method. Correspondence between
Observer fields and Subject fields can be observed by following
the dependence paths: HelpHistoryModelListener._back is
the observed result of target HelpHistoryModel.historyPos, and
HelpHistoryModelListener._forward is th e observed result of
targets HelpHistoryModel.history and HelpHistoryModel.historyPos.
Context has an adapting method, which delegates its duty to
State, while in Strategy pattern, Context only invokes the algorithm
encapsulated in Strategy, the invocation doesn't need to
be an adapting method. This identification standard is not apparent
according to descriptions in [1], but it brings good identification
result in our experiment.
JDP-Detector identifies 37 Strategy instances, 35 of which
are TP, and the precision is 94.6%. It also identifies 8 State
instances, 6 of which are TP, and the precision is 75%.
Table I shows the recognized State instances. Manual analysis
reveals that instances 3 and 8 in Table I are Adapter and
Strategy instances respectively.
TABLE I.
STATE INSTANCES IDENTIFED BY JDP-DETECTOR
Context State Manual
1 AbstractConnector Figure T
2 AbstractTool DrawingView T
3 ConnectionHandle Figure F
4 LocatorHandle Locator T
5 PolygonHandle Locator T
6 StandardDrawingView Drawing T
7 StandardDrawingView DrawingEditor T
8 StandardDrawingView Painter F
Figure 3. An Observer Instance in JEdit 4.3.2
V. EXPERIMENT
This section reports the results of pattern instance identification
in JHotDraw 5.1 (8.3 KLOC, 173 classes) using JDP-
Detector, and compares it with the results reported by some
other researches taking JHotDraw 5.1 as experimental subject.
A. Identification of Adapter Pattern Instances
JDP-Detector finds 17 Adapter instances, 14 of which are
TP (true positive), and the precision is 82.35%. The 3 FP (false
positive) are two Strategy instances and a Decorator instance.
These patterns' structures are similar to that of Adapter, and
adapting behavior is permitted to exist in the two patterns.
M. von Detten et al. obtain 67 instances using fuzzy metric
[13]. They do not manually check the result, only figure out
that the result contains some repeated instances, and instances
with low membership degree is not eliminated. A. De Lucia et
al. get 41 instances using structure information [8]. N. Tsantalis
et al. identify pattern instances using similar scoring of structure
graph, and recognizes 23 instances without distinguishing
Strategy pattern from Command pattern [5]. Result of [5] is a
subset of that of [8], and our result is a subset of that of [5]. In
other words, we adopt a stricter identification standard.
B. Identification of State and Strategy Instances
State instances and Strategy instances are difficult to distinguish
in existing researches. We believe that, in State pattern,
Due to space limitation, the recognized Strategy instances
can't be listed, two FP of which are Command and Adapter
instances respectively. The Command instance is recognized
because it has similar structure with Strategy pattern. The
Adapter instance (ChangeConnectionHandle/Figure) recognized
as Strategy instance is the same one as item 3 of Table I, as
ChangeConnectionHandle is a subclass of ConnectionHandle.
This FP is identified because JDP-Detector fails to take the
inherited methods of the analyzed class into account.
N. Tsantalis et al. identify 22 instances using DPD without
distinguishing State from Strategy [5]. Y. Guéhéneuc et al.
infer structure characteristic of design pattern using machine
learning and get 2 State instances and 4 Strategy instances [6].
A. De Lucia et al. get 43 Strategy instances (the precision is
46.51%) and 36 State instances (the precision is 5.6%) using
both structure information and method call sequence information
[7]. Based on the comparison, we can conclude that our
approach is much more successful in distinguishing State instances
from Strategy instances.
C. Identification of Observer Instances
As illustrated in Table II, JDP-Detector identifies 4 Observer
instances, and reports the notified event objects in each instances.
Three of them are TP, and the precision is 75%. Instance
4 in Table II is a FP, which contains event notifying
behavior, but no one-to-many broadcast behavior.
A. De Lucia et al. identify 9 Observer instances using their
tool DPRE [7]. Five of the nine instances are TP, and the precision
is 55.56%. We compare these instances with our result,
and find that three of their instances (Drawing/Drawing-
ChangeListener, StandardDrawing/DrawingChangeListener,
Drawing/DrawingView) are actually the same one as instance 2
of Table II. Moreover, we find a instance (Connector/ConnectorFigure)
identified by DPRE is not a TP, because it neither
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