Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology seems to be promising in data intensive software requirements variability specification.

1. REQUIREMENTS VARIABILITY SPECIFICATION FOR DATA INTENSIVE
SOFTWARE
http://aircconline.com/ijsea/V10N2/10219ijsea03.pdf
International Journal of Software Engineering & Applications (IJSEA)
ERA Indexed
ISSN: 0975 - 9018 (Online); 0976-2221 (Print)
http://www.airccse.org/journal/ijsea/ijsea.html
Philadelphia University Faculty of Information Technology
Department of Computer Science

2. OutlineOutline
Study Problem
• Current State, Solved Insufficiencies
Proposed Approach
• Bio-Inspired Modeling, Feature-Based Variability
Modeling in Data requirements
 Evaluation and Conclusion

3. Study ProblemStudy Problem
Figure1. A Feature Model of Student-Course Relations.
Current State
(Abo Zaid and Troyer, 2011; Khedri and Khosravi, 2013; Khedri and Khosravi, 2015; Bartholdt et al., 2009)

4. Solved Insufficiency:Solved Insufficiency: Data Versioning with FMData Versioning with FM
Figure 2. Variable Conceptual Feature Model of Student-Course Relation

5. Feature-Based Variability Modeling in DatabasesFeature-Based Variability Modeling in Databases
(MVMD)(MVMD)
Figure 3. A Bio-Inspired Methodology for Feature-based Variability Modelling in database, using UML notation.
EBNF notation
(Junghans et al., 2007).
Algebraic
Specification
(Sannella and Tarlecki, 2012).
Combination between EBNF
and Algebraic Specifications
(Ibraheem and Ghoul, 2016).
+
To present the
architecture of the
feature model
To present the
behaviour of the
feature model
To present the
architecture and
behaviour of the feature
model

6. Variable Database Feature ModelVariable Database Feature Model
1
2
3
Variable Conceptual Feature Model
Variable Logical Feature Model
Physical Feature Model
A
B

7. Variable Conceptual Feature ModelVariable Conceptual Feature Model
Variable Schema Definition Feature Model
DB models Variability
Conceptual model Variability
n..mVSDFM Variability
Relation Variability
Version Variability
Revision Variability

8. SummarySummary
Variable Database feature model enhanced throughVariable Database feature model enhanced through
adding:adding:
Variable Conceptual Feature Model
Variable Logical Feature Model
Physical Feature Model

9. Variable Database Feature ModelVariable Database Feature Model
1
2
Variable Conceptual Feature Model
Variable Logical Feature Model
A
B

10. Variable Conceptual Feature ModelVariable Conceptual Feature Model
Variable Schema Relation Feature Model
n..m

11. SummarySummary
 Variable Schema Relation feature model enhancedVariable Schema Relation feature model enhanced
through addingthrough adding Import relation as featureImport relation as feature

12. Variable Database Feature ModelVariable Database Feature Model
1
2
Variable Conceptual Feature Model
Variable Logical Feature Model
A
B

13. Variable Logical Feature ModelVariable Logical Feature Model
V1-R1-LM
{
Relations Name: Student, Course.
Versions Name: V1-primary of student, V1-primary of course
relations.
Revisions Name: R2 of student relation, R1 of course relation.
}
Figure 4. Example of an application needs in a Textual representation.
Application Requirements Example

14. Variable Logical Feature ModelVariable Logical Feature Model
n..m
< Revision LM features >= “Revision LM”: < Revision LM features name>;
<Application need features>;

15. SummarySummary
 Variable Logical feature model build based onVariable Logical feature model build based on
Application Requirements.Application Requirements.
 Variable Logical feature model enhanced throughVariable Logical feature model enhanced through
satisfying application needs and by adding:satisfying application needs and by adding:
Version Features
Revision Features

16.  Strongly typed Database Management System (DBMS).
 DB languages.
• Software product lines.
• Multiple software product lines.
• Data intensive product lines.
This approach is recommended to be used in any variable data feature
modeling area like:
EvaluationEvaluation
Real examples: eHealth systems and Registration systems in universities.
Implementation issuesImplementation issues:
Application areas:Application areas:

17. (Bartholdt
et al.,
2009).
(Abo Zaid
and
Troyer,
2011).
(Khedri and
Khosravi,
2013).
(Khedri and
Khosravi,
2015).
Paper
Methodology.
9
EvaluationEvaluation
Strong
Not supported
1. Feature model enhancement with powerful concepts.
1     
2. Variability at the first two levels.
2    
3     
3. Using bio inspired concepts for building methodology.
4    
4. Modelling data variability using version and revision
techniques for database schema definitions variability as well as
application needs.
.
    5
5. Composing database schema based on application needs in a
coherent way.
    6
6. Clear methodology based on formal languages.
7. Decreasing data maintenance efforts
    7

18. ConclusionConclusion
Variable Database feature model enhanced throughVariable Database feature model enhanced through
adding:adding:
Variable Conceptual Feature Model
Variable Logical Feature Model
Physical Feature Model
Variable Schema Relation Feature Model

19. ConclusionConclusion
Each feature model presented using :Each feature model presented using :
•Graphical Representation
•Formal languages as a combination between:
EBNF Notations
Algebraic Specification

20. ConclusionConclusion
Natural life Database systems Database as a natural phenomenon

21. PerspectivesPerspectives
 Handling variability in the physical layer through feature
modeling, versions and revisions and formalization
techniques.
 Automating the process of generating feature model for
variable requirement.
 Database applications programming in the new data
variability approach