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Context. In 2007, Aversano et al. [2] analysed the evolution of JHotDraw, ArgoUML, and Eclipse JDT between years 2000-2005 to understand the role of frequently changed design patterns. Goal. In this paper, we perform a replication of the study on more recent versions to control for artifactual results. In particular, we investigate whether maturity of software versions can affect the original results. Method. We perform a re-analysis of the original data to learn and correctly deploy the tools used for data collection and analysis and to control instrumental threats that typically affect a replication. Results/Conclusions. Findings confirm that patterns change more frequently when they play a crucial role in the software and when in newer releases they support more advanced features.
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Evolution of Design Patterns: a Replication Study
Bruno Rossi
Department of Computer Systems and
Communications,
Masaryk University, Brno, Czech Republic
brossi@mail.muni.cz
Barbara Russo
Faculty of Computer Science,
Free University of Bozen-Bolzano, Bolzano, Italy
brusso@unibz.it
ABSTRACT
Context. In 2007, Aversano et al. [2] analysed the evolution
of JHotDraw, ArgoUML, and Eclipse JDT between years
2000-2005 to understand the role of frequently changed de-
sign patterns. Goal. In this paper, we perform a replication
of the study on more recent versions to control for artifac-
tual results. In particular, we investigate whether maturity
of software versions can affect the original results. Method.
We perform a re-analysis of the original data to learn and
correctly deploy the tools used for data collection and analy-
sis and to control instrumental threats that typically affect a
replication. Results/Conclusions. Findings confirm that
patterns change more frequently when they play a crucial
role in the software and when in newer releases they sup-
port more advanced features.
Categories and Subject Descriptors
D.2.2 [Software Engineering]: Design Tools And Tech-
niques—Object-oriented design methods
General Terms
Design, Experimentation
Keywords
Replication, Design Patterns, Theory Validation
1. INTRODUCTION
In 2012, Zhang and Budgen [13] analyzed a large amount
of empirical studies on design patterns. They found scarce
evidence and limited rigor in reporting empirical findings.
As such, they recommended increasing the rigor of obser-
vational studies to explicit links between conclusions and
reported experience on the use of design patterns.
Replications of empirical studies can help on this by in-
creasing internal validity or external generalisation of empir-
ical research [7]. Replicating studies by changing dataset,
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method, subjects, or analysis consolidates results and in-
creases conclusions’ accuracy. Replications can be performed
at different levels of complexity, from re-analysis of the same
data to experiment replication.
The replication we propose mines large amount of data re-
trieved from code repositories to investigate change prone-
ness of design patterns. We replicated the study of Aver-
sano et al., [2], at a later stage of maturity of the same
systems. We performed what is called replication to Con-
trol for Artifactual Results, [8], in that we want to verify
that results reflect reality and are not a product to specific
software versions or experimenters. We ran an independent
external replication, [1], [11], where the original and the new
group of experimenters are completely different and inde-
pendent. We use the technique of re-analysis of the original
data to get confidence with the apparatus of the replication
and validate the replication setup. Finally, we compare our
results to find that we can only partially confirm the origi-
nal results. In reporting the replication, we follow Carver’s
guidelines [4].
2. RELATED WORK
Whether the evolution of design patterns can provide use-
ful insight into the maintenance process is under investiga-
tion. According to Gamma et al. [6] design patterns make
software robust as they let aspects of software structure vary
independently. In other words, in principle, with patterns,
code changes do not propagate in the code uncontrollably.
On the other hand, Posnett et al. [10] argue that simply
the size of classes more than the role of design patterns de-
termines the proneness of software classes to be changed.
Given the amount of data to mine, literature often stud-
ies the evolution of design patterns of one-two consecutive
versions, e.g., [2, 5]. Bieman et al. [3] is one of the few ex-
amples that investigates design patterns between software
versions distant in time. They found that classes that play a
role in design patterns are more change prone both for early
and newer versions when the project is not only maintained
by the Open Source community. For the project JRefac-
tory completely maintained by the Open Source community,
newer versions do not show a relation between the role in
design patterns and their change proneness.
3. THE ORIGINAL STUDY
The major objective of the study of Aversano et al., 2007,
is to empirically investigate pattern change proneness [2].
Objects. The study analyses few early releases of three
Java Open Source Software projects, JHotDraw (5.2-5.4B2),
ArgoUML (0.9-0.20), and Eclipse-JDT (1.0 - 3.0) that can
be classified as small, medium, large project, respectively.
JHotDraw and ArgoUML are completely supported by the
Open Source community.
Tools. Design patterns are collected with the tool of
Tsantalis et al. [12] that runs an algorithm to match nodes
in graphs by similarity scores. To determine in which snap-
shot a class participating to a pattern changed, the authors
used a Perl script supported by a fact extractor based on
the Javacc parser generator that reconstructs facts about
the committed source code.
Procedure. To determine the change history of design
patterns, the authors inspected the versioning system repos-
itories (CVS) of the projects. They extracted the change
sets, i.e., a sequence of file revisions that share the same au-
thor and commit note within a time window of 200 seconds,
referenced as Snapshots. The authors reconstruct history of
pattern evolution across snapshots.
Design. The authors introduce types of pattern change
and analyse them over snapshots. Frequencies of changes
across snapshots are reported by type of change and pattern.
Finally, authors use box plots and statistical proportion tests
across snapshots to identify patterns that mostly change and
identify the type of these changes.
Findings. The paper reports findings that are statisti-
cally significant for the following research questions:
RQ1: How frequently do patterns change across re-
leases? Patterns change more frequently when they play a
crucial role for the intent of the application. The most fre-
quently changed patterns are: Observer in JHotDraw that
manages the update of a figure, Command and Decorator in
ArgoUML that support the execution of new features for the
user menu while Adaptors are used by interfaces of the UML
meta-model to visualise them in Swing tables, and Visitors
in Eclipse-JDT that are used in the navigation of Abstract
Syntax Trees in Java.
RQ2: What kind of changes are different patterns
subject to? Changes to the pattern interfaces make the
pattern less resilient to changes. Method interface changes,
or addition/deletion of methods, and addition/deletion of
pattern subclasses cause higher impact.
RQ3: How much does source code co-change with
patterns? Creational patterns exhibit more co-change than
other patterns and patterns crucial to the application role
have more co-change than other patterns. When a pattern
is crucial for the application purpose, it tends to change
together with a large number of other classes.
4. STUDY REPLICATION
According to Mittelstaedt and Zorn [9], this study is a
Type III replication as we apply the same models, statistical
methods and variables of the original work to different data.
In particular, the replication data belongs to a newer version
of the same software. Our replication includes a re-analysis
of the original data as defined in Gomez et al. [8] to ensure
that findings are independent from the procedure or setup.
4.1 Replicating design
The study replication consists of two stages: 1) re-analysis
of the a subset of the original data to validate the setup and
tune the tools 2) replication of the original analysis with
newer versions. Table 1 illustrates the replication design
according to the framework of Gomez et al. [8].
4.1.1 Re-analysis of original data
Objects. We chose to re-analyse data of JHotDraw be-
ing the smallest project. We selected the same releases of
JHotDraw5 (releases 5.2 - 5.4B2) using the CVS repository.
After a private communication with Nikolaos Tsantalis and
the authors of the original paper, we were able to extract an
identical sample.
Tools. We wanted to ensure that tools are selected and
tuned as in the original work. We first used the same ver-
sions of the Design Pattern Detection (DPD) tool of Tsan-
talis et al. [12] and a fact extractor as in the original paper.
We then run the newest versions of the tools that provide
better accuracy according to their change logs1.
Results. Using the same versions of the tools used in the
original work, we found the same number of design patterns
as in the original work. This ensured that we properly con-
figured the tools and in parallel verified the Java scripts we
wrote to integrate and report the numerical findings. Using
the newest versions of the tools, we found slightly different
values for some patterns, though. Specifically, we have a
lower number of the Prototype pattern and Observer and
greater number for the Adapter-Command pattern, Decora-
tor, and State-Strategy. Given the little difference and the
better accuracy, we decided to adopt the newest versions in
the replication and keep the difference in mind before draw-
ing our conclusions.
4.1.2 Replicating the Pattern Analysis Process
To replicate the analysis process, we followed the steps in
the original order.
Step 1: Detecting Design patterns. As in the original
paper, we use the DPD tool version 4.5 [12].
Step 2: Reconstructing pattern evolution history
across releases. We adopted exactly the same rationale
for the evolution of a pattern. We assume that a pattern
instance in snapshot jrepresents the evolution of a pattern
instance in snapshot j1 if and only if (i) the type of pattern
is the same; and (ii) at least one of the main participant
classes of the pattern is the same class in both snapshot
j1 and j.
Step 3: Snapshots extraction and co-change iden-
tification. We followed the same procedure to define snap-
shots (Section 3). As in the original study, we compute the
number of snapshots where at least one class belonging to
a pattern instance has changed and divided it by the total
number of snapshots.
Step 4: Locating pattern changes and determining
the kind of change. We used the same approach as in the
original study to determine the kind of change that has been
performed in a snapshot. We compare class Ci,j at snapshot
j, with the same class Ci,j1at the snapshot j1,with the
fact extractor. Once facts are identified, we build a Java
script to detect the differences among facts between Ci,j
and Ci,j1.
Step 5: Analyzing pattern co-change. A co-change
is code that changes contextually with the pattern. As in
the original study, we analyse co-changes in terms of lines of
code (additions / deletions).
1http://users.encs.concordia.ca/~nikolaos/pattern_
detection.html
Table 1: The replication design according to the framework of Gomez et al. [7]
Structure type Structure Description Purpose Purpose Description
Site Same Open Source Projects, but newer versions Type Control for artifactual results
Experimenters External replication: different independent re-
searchers
Setup Change site and experimenters
Apparatus Retrosp ective study, same analysis process, col-
lection of design patterns, and statistical tools
Goal To verify that results are not a product of the
specific software versions used and experimenters
Operationalization Same variables Type of validity Internal
Population Props Unchanged. Design patterns are collected from
the same Open Source projects
4.1.3 Site
We considered the three OSS projects selected in the orig-
inal paper. The original paper analyzed those releases in
which the number of classes increased the most, actually
tripled for all projects (Table 1, in [2]). Those releases per-
tain to the initial period of development where there are
large fluctuations of number of classes. To analyse the im-
pact of maturity on the projects, we select four consecutive
recent versions in which the total number of classes either
stays constant or even decreases. As such, we consider JHot-
Draw 7 (7.1 - 7.31, Mar2008 - Oct2009), ArgoUML (0.32 -
0.34, Jan2011 - Dec2011) and Eclipse JDT Core and UI (3.5
- 3.6, Jun2009 - Jun2010).
Counting Snapshots. As we mentioned, the different
versioning systems might have caused some differences in the
number of snapshots. In JHotDraw 5, for example, the CVS
repository is not available anymore as it has been migrated
to SVN. This migration has reduced the original number
of commits. For this reason, the number of snapshots we
were able to compute (124) is smaller than the corresponding
number in the original study (177).
5. RESULTS
In this section we use ANOVA and proportion tests to de-
rive our findings and compare them with the original study2.
RQ1: How frequently do patterns change across
releases? As in the original paper, the ANOVA statisti-
cal test shows that some patterns change more frequently
than others (Fig. 1): JHotDraw5 (p-value = 1.7e05), JHot-
Draw7 (p-value = 6.62e09 ), ArgoUML (p-value = 2.02e07),
Eclipse JDT Core (p-value = 3.1e11), Eclipse JDT UI (p-
value = 6e04).
Table 2: Patterns that frequently change
Project Original Paper Replication
JHotDraw Observer Composite
ArgoUML Adapter-Command Prototype
Eclipse JDT Visitors State-Strategy
Table 2 shows the results of the proportion tests and the
resulting most changed patterns. Again we can see that fre-
quent changed patterns play a crucial role in the application.
The role is changed with the maturity of the project. For
example, in JHotDraw, Composites are the most changing
patterns in newer versions. Namely, Composites are used
for more advanced features as composing existing figures to-
gether. In the newer releases of ArgoUML, Prototypes are
used in the advanced feature that renders (complex) figures
in UML diagrams. Finally, in Eclipse JDT State-Strategy
patterns implement different sorting and filtering algorithms
2Details and boxplots available at http://goo.gl/pfOH57
in viewer elements without interfering with their loading and
filling.
Patterns change more frequently when they play a crucial
role for the intent of the software and the role evolves with
the maturity of the project to support more advanced fea-
tures.
RQ2: What kind of changes are different patterns
subject to? We analyse the kind of changes performed on
the classes belonging to patterns. As in the original paper,
we use proportion tests on the different types of changes
(Fig. 2). For all the projects, method interface and im-
plementation changes dominate over other types of changes,
but unlike the original paper in which method implementa-
tion predominates for JHotDraw and ArgoUML, proportion
tests do not report significant differences among patterns of
these two projects.
The tests report significant result for Eclipse JDT. Un-
like the original paper, we separate the analysis between the
JDT core and UI given the clear separation of concerns of the
two major packages. For Eclipse JDT core, unlike the origi-
nal results in which subclassing predominated, the most fre-
quent changes are method implementation changes (p-value
= 3.951e05), method interface changes (p-value = 0.001),
and additions and removals of class attributes (p-value =
0.001). Method implementations change more in the case
of Observers (prop=100%, but there are only 6 instances)
and Adapter-Command (prop = 80.27%), whereas method
interfaces change more for Prototypes (prop = 28.9%).Ad-
ditions and removals of class attributes change mainly for
Singletons (prop=17.32%). Changes in method implemen-
tations for the Observer and Adapter-Command patterns
are significant in the new as well as in the old releases. In
newer releases, changes in method implementation also oc-
cur in patterns that support the development of the plug-in
architecture. This happens for example for the Adapter-
Command and Prototype patterns that allow plug-ins to be
loosely coupled in the dynamic Eclipse runtime environment,
or for the Factory Methods that are implemented to extend a
Java interface used in plug-in new additions. In Eclipse JDT
UI package, there are significant differences among patterns’
changes only in method implementation (p-value = 0.01)
but not in method interface changes (p-value = 0.46), or
additions and removals of class attributes (p-value = 0.09).
Changes in method implementation are the highest for Fac-
tory Method (prop = 87.50%).
Eclipse JDT is the only project that exhibits clear differ-
ences among the types of changes across design patterns.
Unlike earlier versions, changes in method implementation
predominate the types of changes. Types of changes differ
if patterns belong to Eclipse JDT core or UI package.
RQ3: How much does source code co-change with
patterns?
Table 3: Patterns with larger source code co-change
Project Original Paper Replication
JHotDraw Not decidable Observer
ArgoUML Singletons Not decidable
Eclipse JDT Visitors Not decidable
We compute the number of lines that were added, re-
moved, and modified as co-change of a design pattern. Dif-
ferences are relevant for JHotDraw7 (p-value=1.52e05). The
analysis by type of changes shows that this is due to new
line additions originated with the Observer pattern. Differ-
ences in co-changes in ArgoUML are overall not significant
(p-value=0.90), as well as those of Eclipse JDT Core (p-
value=0.09), and Eclipse JDT UI (p-value=0.71), Table 3.
The analysis by type of changes reports that for Argo UML
Composite, Singleton, Factory Method patterns show more
removals and modifications than other patterns, whereas
Eclipse JDT, both for core or UI, has more additions / re-
movals / modifications when Singletons add or remove their
attributes. The finding on Singletons is the sole confirma-
tion of the original results.
6. CONCLUSIONS
In this paper, we replicated an empirical study on the evo-
lution of design patterns [2]. The aim of the replication was
to determine internal validity of the original empirical re-
sults by applying the same analysis process and experiment
apparatus to newer versions of software. We analysed four
consecutive new versions of JHotDraw, ArgoUML, Eclipse
JDT and we additionally used the old version of JHotDraw
as benchmark to control for the correct instrumentation and
operalisation of the work. We run the same research proto-
col of the original study, also with the help of the authors of
the original paper and researchers that developed the orig-
inal tools for data collection. Findings illustrate that pat-
terns change more frequently when they play a crucial role
in the software and the role evolves with the maturity of the
project to support more advanced features.
In more mature versions, the types of changes are only
clear for Eclipse JDT and the types depend on whether
they refer to JDT core or UI. We have additionally found
that patterns that characterise the architecture of the sys-
tem (Observer and Adapter - Command used for plug-ins)
change the most and change in method implementation in
old as well as in newer releases. As Bieman et al. [3] hy-
pothesised this might be related to the fact that the three
projects have a different support as Eclipse JDT is main-
tained not only by the Open Source community
Finally, projects differ in maturity stages by the type of co-
changes. Eclipse JDT mainly focuses on deleting or modify-
ing existing lines across all design patterns. For less active
projects like JHotDraw7, only specific patterns co-change
with particular frequency.
Summarizing the results from the replication - in cases in
which the results where statistically significant - we can re-
port that also the maturity stage of the project has relevance
on the design patterns that undergo modifications. This
calls for more longitudinal studies on the nature of design
patterns modifications.
Acknowledgements. We thank the authors of the original
paper for the information provided and Nikolaos Tsantalis
for the access to previous versions of the DPD tool.
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Figure 1: Change Frequencies
(a) JHotDraw5 (b) JHotDraw7
(c) ArgoUML (d) Eclipse JDT Core
(e) Eclipse JDT UI
Figure 2: Pattern Changes
(a) JHotDraw5 (b) JHotDraw7
(c) ArgoUML (d) Eclipse JDT
(e) Eclipse JDT UI
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Design patterns are recognized, named solutions to common design problems. The use of the most commonly referenced design patterns should promote adaptable and reusable program code. When a system evolves, changes to code involving a design pattern should, in theory, consist of creating new concrete classes that are extensions or subclasses of previously existing classes. Changes should not, in theory, involve direct modifications to the classes in prior versions that play roles in a design patterns. We studied five systems, three proprietary systems and two open source systems, to identify the observable effects of the use of design patterns in early versions on changes that occur as the systems evolve. In four of the five systems, pattern classes are more rather than less change prone. Pattern classes in one of the systems were less change prone. These results held up after normalizing for the effect of class size - larger classes are more change prone in two of the five systems. These results provide insight into how design patterns are actually used, and should help us to learn to develop software designs that are more easily adapted.
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Goals of replication in empirical software engineering. Experimental replications are performed for the same reasons that independent experiments are conducted, to better understand software engineering phenomena and how to improve the practice of software development. One important benefit of replications is that they help mature software engineering knowledge by addressing both internal and external validity problems. In terms of external validity, replications help researchers show that experimental results are not dependent on the specific conditions of the original study. In terms of internal validity, replications also help researchers show the range of conditions under which experimental results hold. Generally speaking, a successful replication is one that helps the research community build knowledge about which results or observations hold under which conditions (Basili, 1999; Vegas, 2006). Thus, a replication that produces results that are similar to those of the original experiment on which it was based is just as useful to the community as a replication that produces results that are different from those of the original experiment. A replication produces different results is useful because it provides insight to help the community understand why the results were different. Therefore, the success of a replication must be judged relative to the knowledge it contributes to the body of knowledge (for example, identifying possible new variables that have an influence on the response variable). Software engineering is not the only field for which the general experimental concept of replication is important. For example, within the field of Behavioral Research,