Visualizing UML’s Sequence and Class Diagrams Using Graph-Based Clusters

Visualizing UML’s Sequence and
Class Diagrams Using Graph-Based
Clusters
Paper ID 65
Nakul Sharma, Dr. Prasanth Yalla
Department of Computer Science and Engineering
Koneru Lakshmiah Education Foundation
Vaddeswaram,Guntur-522502, India

Agenda
• Abstract
• Introduction
• Literature Review
• Proposed Methodology
• Results & Discussion
• Conclusion & Future Scope

Abstract
The paper discusses the creation of UML diagram based
recommendation system using java and class files as the
input. The existing systems do not make use of
techniques available in text-mining for creating UML
diagrams. The overall methodology makes use of
keyphrase extraction, contextual similarity calculation,
and graph-based clusters in creating UML diagrams. The
existing systems survey of state-of-art UML diagram
generation techniques and keyphrase extraction survey is
also provided. A comparative analysis of the existing
tools for generating UML-diagrams is also provided. The
recommendation system generated is useful to
maintenance engineers and software developers.

Introduction
• In the current work, a literature review of UML diagram
construction from text or source code is done. A
comparative analysis of different methods used in UML
diagram construction is also proposed.
• In this paper, the authors propose a contextual
similarity approach combined with cluster and graph
creation. A multi-step approach involves keyphrase
extraction, graph construction, clustering of related
documents together and finally creation of UML class
& sequence diagrams.

Literature Review
• There are several methodologies being used
in developing UML diagrams. The most
common diagrams which are developed are
use-case and class diagrams.
• However little work has been done wrt using
text, source code, API documentation for
generating UML diagrams. In addition text
mining techniques are not used extensively in
generating UML diagrams.

Existing System Developed (UML Diagram Generation)
Sr. No. Name of Tool Generated Title of Publication Name of Author Publication
Venue
1 Extended ForUML (2019) Extended ForUML for Automatic
Generation of UML Sequence
Diagrams from Object-Oriented Fortran
Aziz Nanthaamornphong,
Anawat Leatongkam
Scientific
Programming, Hindwai
Publications, 2019
2 Automatic Builder of Class Diagram
(ABCD) (2016)
Automatic Builder of Class Diagram
(ABCD): an application of
UML generation from functional
requirements
Wahiba Ben Abdessalem,
Karaa Zeineb Ben Azzouz,
Aarti Singh, Nilanjan Dey,
Amira S. Ashour, Henda Ben
Ghazala
Software Practice and
Experience: Wiley
Publication, 2016
3 RECAA (2015) From requirements to UML models and
back: How automatic processing of
text can support requirements
engineering
Mathias Landhaußer , Sven J.
Korner, Walter F. Tichy
Software Qual
J,Springer, 2014
4 ForUML (2015) Extracting UML Class Diagrams from
Object-Oriented
Fortran: ForUML
Aziz
Nanthaamornphong,Jeffrey
Carver, KarlaMorris,
Salvatore Filippone
Scientific
Programming,
Hindwai Publications,
2015
5 Class-Gen (2010) Parsed use case descriptions as a basis
for object-oriented class model
generation
Mosa Elbendak, Paul
Vickers∗, Nick Rossiter
The Journal of Systems
and Software,
Springer, 2011
6 UMGAR (2008) An Automated Tool for Generating
UML Models from Natural Language
Requirements.
Deeptimahanti, D. K. and
Babar, M. A
IEEE Conference,
2008
7 ER convertor (2008) Heuristics-based entity relationship
modeling through natural language
processing.
Nazlia Omar , Paul Hanna,
and Paul Mc Kevitt
15th Artificial
Intelligence and
Cognitive Science
Conference, Ireland

Input files
Text Pre-
processing
Calculation
of Similarity
Measures
Source
Code
Clustering
Constructing
the UML Class
and Sequence
Diagram using
Clusters
SDG
Representation
Key-phrase
extraction
Overall Architecture Of Proposed
Methodology

Module-1:A Conceptual Dependency Graph Based Keyword Extraction Model for
Source Code to API Documentation Mapping

Algorithm1: Data Filtering
Input : Source code files SC, Class files CF.
Step 1: Read input source codes files SC.
Step 2: Read input class files CF.
Step 3:for each source code SCi in SC[]
Do
Parse source code SCi with methods M and Fields F.
Mi=ExtractMethods(SCi)
Fi=ExtractFields(SCi)
Mapping (Mi , Fi) to SCi
SC1 (M1,F1)
SC2 (M2,F2)
… …..
SCn (Mn,Fn)
done
Step 4: for each class file CFi in CF[]
Do
Parse class files CFi with methods M and Fields F.
Mi=ExtractMethods(CFi)
Fi=ExtractFields(CFi)
Mapping (Mi , Fi) to CFi
CF1 (M1,F1)
CF2 (M2,F2)
… …..
CFn (Mn,Fn)
done

Step 5: // Remove the duplicate methods and fields in each source code and class files
For each code Ci in i j
SC CF

Do
i i j
i i j
M Prob(M M / C);i j
F Prob(F F / C);i j
  
  
If( Mi!=0 AND Fi!=0)
Then
Remove Mi in Ci or Cj
Remove Fi in Ci or Cj
End if
Done
Step 6: //Pre-processing source code comments using Stanford NLP parser.
For each document di in D
Do
T[]=Tokenize(di)
For each token t in T[]
Do
Apply stemming, stopword removal using Stanford NLP library.
Done
Done

Module-2 & 3 Source Code Dependency Graph Based Contextual Probabilistic
Clustering Approach for class dependency Diagrams

Probabilistic Weighted based contextual similarity measure for Source
code and class files dependency graph
Input : Project source codes SC, Project class files CF, Project source metrics
(SMi,SFi) and Project class metrics (CMi,CFi).
Procedure:
Step 1: Read source code metrics , sci(SMi,SFi) and Project class metrics
cfi(CMi,CFi)
Step 2: Constructing a source code dependency graph SDG(V,E) with vertex set V
and Edge set E using source code metrics. Here vertex set V is represented with source
code methods and fields and edge set E is represented as weighted rank between the
vertices.
Step 3: The probabilistic weights of the edges are computed using the vertex terms ti
and tj where i i
t V
 and j j
t V
 .

i, j
i,j i j i j
Prob(t t )
Edgeweight : w(i, j)
2.max{Prob(t ),Prob(t )} Prob(t ,t )




i j
Prob(t , t ) is the number of times both terms i j
(t , t ) occurred
together.
i
Prob(t ) is the number of occurrence of i
t in vertex Vi
j
Prob(t ) is the number of occurrence of j
t in vertex Vj
Step 4: The vertices with positive edge weights are sorted in ascending order in the
dependency graph to find the contextual similarity between the source code metrics.
Step 5: Source code dependency graph SDG is used to find the contextual similarity
between the vertex nodes to the neighbor metrics using the following proposed
measure.
Let U(SMi)  (m1,m2,….mn) denotes the source codes metrics vector at vertex
i.
V(SMj)  (m1,m2,….mr) denotes the source code metrics vector at vertex j.

 
2 2 2
i 1 2 p
2 2 2
1 2 q
i j 1 1 2 2 p q
j
| U(SM ) | U(m ) U(m ) ....U(m )
| | V(m ) V(m ) ....V(m )
| U(SM ).V(SM ) | U(m ).V(m ) U(m ).V(m )... U(m ).V(m )
Pr oposed Contextual source code dependency graph dissimilarity index
is computed as
SM
C
V
 
 
  
1
3
i j i j
i j
U(SM ).V(SM )*tan (| U(SM ) | | V(SM ) |)
SDGDI= ;where i j
2*(| U(SM ) |*| V(SM ) |)
Contextual source code dependency graph similarity index
CSDGSI 1 CSDGDI;

 

 

Contextual source code graph based clustering algorithm
Step 1: Read number of clusters c.
Step 2: Read number of iterations I.
Step 3: Initialize k random clusters as centroids.
Step 4: for each document at vertex V in graph
Do
TF-ID[]= Compute term frequency tf-id
Done
Step 5: Repeat until c clusters
Find nearest cluster distance metrics using the following equation
Let Document vector one V1, document vector two V2
2 2
3
Cosine(V1[i],V2[i])
Dist(V1,V2)
Correlation(V1,V2). V1[i] V2[i]


 
Done
Step 6: Merge the graph nodes using the nearest distance measure.
Step 7: Update cluster centroid using mean distance.
Step 8: Construct the class diagram using the plant UML library to the
filtered top k-clusters C[k].
Step 9: For each source code file SC[i] do
Check the source code file has distance metric >0
If(dist(SC[i],C[k])>0)
Then
Display class diagram in source code file SC[i].
End if
Step 10. done

Step 6: Class file dependency graph CDG is used to find the contextual similarity
between the vertex nodes to the neighbor metrics using the following proposed
measure.
Let U(CMi)  (m1,m2,….mn) denotes the source codes metrics vector at vertex i.
V(CMj)  (m1,m2,….mr) denotes the source code metrics vector at vertex j.
 
2 2 2
i 1 2 p
2 2 2
1 2 q
i j 1 1 2 2 p q
j
| U(CM ) | U(m ) U(m ) ....U(m )
| | V(m ) V(m ) ....V(m )
| U(CM ).V(CM ) | U(m ).V(m ) U(m ).V(m )... U(m ).V(m )
Pr oposed Contextual class code depenedency graph dissimilarity index
is computed as
CM
C
V
 
 
  
3
i j i j
i j
U(CM ).V(CM )*cos(| U(CM ) | | V(CM ) |)
CDGDI= ;where i j
2*(| U(CM ) |*| V(CM ) |)
Contextual class code depenedency graph similarity index
CCDGSI 1 CCDGDI;


 
For each class file in CF[i]
Do
Add to Sequence diagram designer S.
Done
Visualize sequence diagram to all the class files in the given relational packages.

Thursday, March 4, 2021
Key Phrases in SDG :{m_items.iterator()} {m_items.add(i)}  Score :0.9073701027137411
Key Phrases in SDG :{m_items.iterator()} {Collections.sort(m_items)}  Score :0.9073701027137411
Key Phrases in SDG :{m_items.iterator()} {m_items.get(index)}  Score :0.9073701027137411
Key Phrases in SDG :{m_items.iterator()} {m_items.size()}  Score :0.8626786872190586
Key Phrases in SDG :{m_items.iterator()} {m_items.iterator()}  Score :0.826985987428094
Key Phrases in SDG :{m_items.iterator()} {i.hasNext()}  Score :1.0
Key Phrases in SDG :{m_items.iterator()} {i.next()}  Score :1.0
Key Phrases in SDG :{m_items.iterator()} {i.next().toString()}  Score :1.0
Key Phrases in SDG :{m_items.iterator()} {buff.append(i.next().toString() + “ “)}  Score :1.0
Key Phrases in SDG :{i.hasNext()} {Collections.sort(m_items)}  Score :1.0
Key Phrases in SDG :{i.hasNext()} {m_items.add(i)}  Score :0.9073701027137411
Key Phrases in SDG :{i.hasNext()} {Collections.sort(m_items)}  Score :1.0
Key Phrases in SDG :{i.hasNext()} {m_items.get(index)}  Score :1.0
Key Phrases in SDG :{i.hasNext()} {m_items.size()}  Score :1.0
Key Phrases in SDG :{i.hasNext()} {m_items.iterator()}  Score :1.0
Key Phrases in SDG :{i.hasNext()} {i.hasNext()}  Score :0.826985987428094
Key Phrases in SDG :{i.hasNext()} {i.next()}  Score :0.8626786872190586

Result in Form of
Creation of Clusters
Cluster-1{
MultiNomialBMAEstimator.java
SimpleEstimator.java
}
[D@c88a32 = [2]
Cluster-2{
DiscreteEstimatorBayes.java
}
[D@17c2f4f = [0]
Cluster-3{
BayesNetEstimator.java
}
[D@80cdf3 = [1]
Cluster-4{
BMAEstimator.java
}
[D@f9296d = [3]
Cluster-5{
DiscreteEstimatorFullBayes.java

Results in form of Diagrams
Generated
• Class Diagram Sequence Diagram

Visualizing UML’s Sequence and Class Diagrams Using Graph-Based Clusters

Analysis of Existing Systems
Name of UML Tool Techniques / Input files
used for Conversion
NLP
SOFTWARES
NLP AND Rules (Heuristics) XMI/XML
Representation
Source Code API Documentation
Automatic Builder of Class
Diagram (2016)
No Yes Yes No Yes
RECAA (2015) Yes Yes No No Yes
CM-Builder (2000) Yes Yes No No No
UMGAR (2008) Yes Yes No No No
SENSE (2007) Yes Yes No No No
ER convertor (2008) No Yes No No No
LIDA (2001) No Yes No No No
ForUML (2015) Yes Yes Yes Yes No
Extended ForUML (2019) Yes Yes Yes Yes No
SDG Graph Based Yes Yes No Yes Yes

Conclusion
• The paper discusses how UML diagram can be
used as a tool for recommending most
essential classes within a given set of project.
A large-scale open source project cannot be
assessed using the existing similarity
measures. Hence, a new hybrid probabilistic
model is proposed for large open-source
projects

References
• Radoslav Kirkov, Gennady Agre, “Source Code Analysis – An Overview”, Cybernetics And Information Technologies, Volume 10, No 2, Bulgarian
Academy Of Sciences, 2010.
• Mohammed J. Zaki, Wagner Meira Jr., “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Chapter 13, page 370.
• “About the Unified Modeling Language Specification Version 2.5” , https://www.omg.org/spec/UML/2.5/About-UML/
• Nakul Sharma, Prasanth Yalla, “A Hybrid Weighted Probabilistic based source code graph clustering algorithm for class diagram and sequence
diagram visualization”, --Under Review.
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2016, June 8-10, 2016, Baltimore, USA, ISBN: 978-1-5090-0809-4
• B.A.K.Wahiba, B.A. Zeineb. S.Aarti. D.Nilanjan. A.Amira. B.G. Henda. Automatic builder of class diagram (ABCD): an application of
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• Mathias Landha ̈ußer • Sven J. Ko ̈rner • Walter F. Tichy, "From Requirements to UML Models & Back : How automatic processing of text can support
requierments engineering", Software Qual J, DOI 10.1007/s11219-013-9210-6, pp 1-29.
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Hindawi Publishing Corporation, Scientific Programming, Volume 2015, 15 pages, http://dx.doi.org/10.1155/2015/421816
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Fortran" Hindawi, Scientific Programming, Volume 2019, https://doi.org/10.1155/2019/2542686

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