TOP 10 SOFTWARE ENGINEERING PAPERS:
RECOMMENDED READING – SOFTWARE ENGINEERING & APPLICATIONS S RESEARCH
E-GOVERNMENT MATURITY
MODELS: A COMPARATIVE STUDY
Abdoullah Fath-Allah1, Laila Cheikhi1, Rafa E. Al-Qutaish2, and
Ali Idri1
1Software Project Management Research Team, ENSIAS - Mohammed V
University -
Souissi, Rabat, Morocco
2 Dept. of Software
Engineering & IT, École de Technologie Supérieure, University of
Québec, Montréal, Canada
ABSTRACT
Many maturity models have been used to assess or rank e-government
portals. In order to assess electronic services provided to the citizens, an
appropriate e-government maturity model should be selected. This paper aims at
comparing 25 e-government maturity models to find the similarities and
differences between them and also to identify their weaknesses and strengths.
Although the maturity models present large similarities between them, our
findings show that the features included in those models differ from a maturity
model to another. Furthermore, while some maturity models are covering some
features and introducing new ones, it seems that others are just ignoring them.
KEYWORDS
E-government,
portal, maturity model, comparison, best practices, e-services, maturity
stages.
REFERENCES
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M., Pardo, T. A., & Canestraro, D. S. (2006). Digital capability assessment
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[5] Andersen, K. V., & Henriksen, H. Z. (2006).
E-government maturity models: Extension of the Layneand Lee model. Government
Information Quarterly, 23(2), 236–248.
[6] United-Nations. (2012). UN E-Government Survey 2012:
E-Government for the People. Retrieved from
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http://unpan1.un.org/intradoc/groups/public/documents/un/unpan048065.pdf
[7] Alhomod, S. M., Shafi, M. M., Kousarrizi, M. N., Seiti,
F., Teshnehlab, M., Susanto, H., Batawi, Y. A. (2012). Best Practices in E
government: A review of Some Innovative Models Proposed in Different Countries.
International Journal of Electrical & Computer Sciences, 12(01), 1–6.
[8] Hiller, J. S., & Belanger, F. (2001). Privacy
strategies for electronic government. E-Government, 200,162–198.
[9] Almazan, R. S., & Gil-Garcia, J. R. (2008).
E-Government Portals in Mexico. Retrieved fromhttp://www.igi-global.com/chapter/electronic-government-concepts-methodologies-tools/9818
[10] Cisco IBSG. (2007). e-Government Best Practices learning
from success, avoiding the pitfalls. Retrievedfrom
http://siteresources.worldbank.org/EXTEDEVELOPMENT/Resources/20080222_Phil_eGov_workshop.pdf?resourceurlname=20080222_Phil_eGov_workshop.pdf
http://siteresources.worldbank.org/EXTEDEVELOPMENT/Resources/20080222_Phil_eGov_workshop.pdf?resourceurlname=20080222_Phil_eGov_workshop.pdf
[11] Baum, C., & Di Maio, A. (2000). Gartner’s four
phases of e-government model. Gartner Group.
[12] West, D. M. (2004). E-Government and the Transformation
of Service Delivery and Citizen Attitudes.Public Administration Review, 64(1),
15–27.
[13] Moon, M. J. (2002). The Evolution of E-Government among
Municipalities: Rhetoric or Reality? Public Administration Review, 62(4),
424–433.
[14] Toasaki, Y. (2003). e-Government from A User’s
Perspective. APEC telecommunication and information working group, Chinese
Taipei.
[15] Deloitte Consulting, & Deloitte & Touche.
(2000). At the dawn of e-government: The citizen as customer. New York:
Deloitte Research. Retrieved fromhttp://www.egov.vic.gov.au/pdfs/egovernment.pdf
[16] Howard, M. (2001). E-government across the globe: how
will’e’change government. E-Government, 90,80.
[17] Shahkooh, K. A., Saghafi, F., & Abdollahi, A.
(2008). A proposed model for e-Government maturity. In Information and
Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd
International Conference on (pp. 1–5). Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?
arnumber=4529948
[18] Lee, G., & Kwak, Y. H. (2012). An Open Government
Maturity Model for social media-based publicengagement. Government Information
Quarterly. Retrieved fromhttp://www.sciencedirect.com/science/article/pii/S0740624X1200086X
[19] Siau, K., & Long, Y. (2005). Synthesizing
e-government stage models–a meta-synthesis based on metaethnographyapproach.
Industrial Management & Data Systems, 105(4), 443–458.
Citation
Count –22
PROGRAM SLICING
TECHNIQUES AND ITSAPPLICATIONS
N.Sasirekha1, A.Edwin Robert2and Dr.M.Hemalatha3
1Doctoral Research Scholar, Karpagam University, Coimbatore,
Tamilnadu, India
2Doctoral Research
Scholar, Karpagam University, Coimbatore, Tamilnadu, India
3Head, Department of
Software Systems, Karpagam University, Coimbatore, Tamilnadu,India.
ABSTRACT
Program understanding is an important aspect in Software
Maintenance and Reengineering. Understanding the program is related to
execution behaviour and relationship of variable involved in the program. The
task of finding all statements in a program that directly or indirectly
influence the value for an occurrence of a variable gives the set of statements
that can affect the value of a variable at some point in a program is called a
program slice. Program slicing is a technique for extracting parts of computer
programs by tracing the programs’ control and data flow related to some data
item. This technique is applicable in various areas such as debugging, program
comprehension and understanding, program integration, cohesion measurement,
re-engineering, maintenance, testing where it is useful to be able to focus on
relevant parts of large programs. This paper focuses on the various slicing
techniques (not limited to) like static slicing, quasi static slicing, dynamic
slicing and conditional slicing. This paper also includes various methods in
performing the slicing like forward slicing, backward slicing, syntactic
slicing and semantic slicing. The slicing of a program is carried out using
Java which is a object oriented programming language.
KEYWORDS
Amorphous slicing,
Backward slicing, Conditioned slicing, Debugging, Dynamic slicing, Forward
slicing, Functional Cohesion, Program Slicing, Quasi Static slicing, Static
slicing.
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Citation
Count – 21
DESIGNING CODE LEVEL REUSABLESOFTWARE
COMPONENTS
B.JALENDER 1, Dr A.GOVARDHAN 2, Dr P.PREMCHAND 3
1 Asst Professor, Department of IT, VNRVJIET, Hyderabad,
India-500090
2Professor in CSE &
Director of Evolution, JNTUH, Hyderabad,.
3Professor ,CSE
Department,UCEOU, Osmania University, Hyderabad.
ABSTRACT
The basic idea behind building Reusable software components
is to design interchangeable parts from other industries to the software field
of construction. A reuse library or component reuse repository organizes stores
and manages reusable components. The biggest advantage of the building reusable
software components is that it reduces the time and energy in developing any
software. Frameworks provides a standard working system through which user ‘s
main focus is on developing desired modules instead of developing lower level
details. By using this facility the software developers can spend more time in
developing the requirement of software, rather than preparing the tools of
application development. Framework is set of reusable software program that
forms the basis for an application. Frameworks help the programmers to build
the application quickly .At its best code reuse is accomplished through the
sharing of common classes and/or collections of functions, frameworks and
procedures. This paper describes how to build the code level reusable
components and how to design code level components. Finally providing coding
guidelines, standards and best practices used for creating reusable code level
components and guidelines and best practices for making configurable and easy
to use.
KEYWORDS
Reuse, code,
component, barries, software, framework.
REFERENCES
[1] B.Jalender, Dr A.Govardhan, Dr P.Premchand “A Pragmatic Approach To Software Reuse”, 3 vol 14No 2 Journal of Theoretical and
Applied Information Technology (JATIT) JUNE 2010 pp 87-96.
[2] B.Jalender, Dr A.Govardhan and Dr P.Premchand. Article:
Breaking the Boundaries for SoftwareComponent Reuse Technology. International
Journal of Computer Applications 13(6):37–41, January 2011. Published by
Foundation of Computer Science.
[3] Article “Considerations to Take When Writing Reusable
Software Components”
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Engineering with Reusable Designs and Code,” IEEE Transactions on Software Engineering, vol. SE-10, no.
5, September 1984, pp. 498-501
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Reusability through Program Transformation,” IEEE Transactions on Software Engineering, vol. SE-10, no.
5, September 1984, pp. 574-588.
[6] T.J. Biggerstaff and A.J. Perlis, eds.,” Software Reusability:
Concepts and Models” ACM Press, NewYork, vol. 1, 1989.
[7] B.Jalender, Dr A.Govardhan, Dr P.Premchand, Dr
C.Kiranmai, G.Suresh Reddy” Drag and Drop: Influences on the Design of Reusable
Software Components” International Journal on Computer Science and Engineering
Vol. 02, No. 07, pp. 2386-2393 July 2010.
[8] B.Jalender, N.Gowtham, K.Praveenkumar, K.Murahari,
K.sampath”Technical Impediments to Software Reuse” International Journal of Engineering
Science and Technology (IJEST) , Vol. 2(11),p. 6136- 6139.Nov 2010.
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Reusable Software Modules, Ph.D. dissertation, Department of Computer and Information
Science, The Ohio State University, Columbus, OH, June 1989.
[10] B.Jalender, Reddy, P.N. “Design
of Reusable Components using Drag and Drop Mechanism” IEEE Transactions on Information Reuse
and Integration. IEEE International Conference IRI Sept. 2006 Pages: 345 – 350.
[11] B.H. Liskov and S.N. Zilles, “Specification Techniques for Data Abstractions,” IEEE Transactions on Software
Engineering, vol. SE-1, no. 1, March 1975, pp. 7-19.
[12]Sullivan,K.J.;Knight,J.C.;“Experience assessing an architectural approach to
large-scale, systematic reuse,” in Proc. 18th Int’l Conf. Software Engineering, Berlin,
Mar. 1996, pp. 220–229
[13] Schmidt, D. C., Why Software Reuse has Failed and How to
Make it Work for You [Online], Available: http://www.flashline.com/content/ DCSchmidt/lesson_1.jsp, [Accessed: 18 August 2002].
[14] Douglas Eugene Harms “The
Influence of Software Reuse on Programming Language Design” The Ohio State University 1990.
[17] “Breaking Down the Barriers to Software Component Technology” by Chris Lamela IntellectMarket, Inc
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The generic reusable component: an approach to reuse hierarchical OO designs” appears in: software reusability,1993
[19] Charles W. Krueger Software Reuse “ACM
Computing Surveys (CSUR) Volume 24, Issue 2 (June 1992) Pages: 131 - 183.
[21] M. Pat Schuler, “Increasing productivity through Total Reuse Management (TRM),” Proceedings of Technology2001: The
Second National Technology Transfer Conference and Exposition, Volume 2,
Washington DC, December 1991, pp. 294-300.
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Monterey CA, Oct. 3-5, 1989
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Bianco, Sharon S. Paulson, Robert L. Binkley, Yvonne D.Kellogg, Chris J.
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Syed, “The NASA Technical Report Server”, Internet Research: Electronic Network
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Citation
Count – 21
FORMALIZATION OF THE
DATA FLOW DIAGRAM RULES FOR CONSISTENCY CHECK
Rosziati Ibrahim and Siow Yen Yen
Department of Software Engineering, Faculty of Computer Science
and Information
Technology, Universiti
Tun Hussein Onn Malaysia (UTHM),
Parit Raja, 86400, Batu
Pahat, Johor Malaysia.
ABSTRACT
In system development life cycle (SDLC), a system model can
be developed using Data Flow Diagram (DFD). DFD is graphical diagrams for
specifying, constructing and visualizing the model of a system. DFD is used in
defining the requirements in a graphical view. In this paper, we focus on DFD
and its rules for drawing and defining the diagrams. We then formalize these
rules and develop the tool based on the formalized rules. The formalized rules
for consistency check between the diagrams are used in developing the tool.
This is to ensure the syntax for drawing the diagrams is correct and strictly
followed. The tool automates the process of manual consistency check between
data flow diagrams.
KEYWORDS
Consistency Check,
Context Diagram, Data Flow Diagram, Formal Method.
Volume Link : http://www.airccse.org/journal/ijsea/vol1.html
REFERENCES
[1] Ahmed Jilani, A. A., Nadeem, A., Kim, T. H. and Cho, E.
S. (2008). Formal Representations of the Data Flow Diagram: A Survey. Proc. of
the 2008 Advanced Software Engineering and Its Applications. Washington, USA:
IEEE Computer Society. pp. 153-158.
[2] Lucas, F.J., Molina, F. and Toval, A. (2009). A
Systematic Review of UML Model Consistency Management. Information and Software
Technology, 51(12), pp. 1 – 15.
[3] Dennis, A., Wixom, B.H. and Roth, R.M. (2006). Systems
Analysis and Design. 3rd ed. Hoboken: John Wiley & Sons, Inc.
[4] Dixit, J. B. and Kumar, R. (2008). Structured System
Analysis and Design. Paperback ed. New Delhi, India: Laxmi Publisher.
[5] Tao, Y.L. and Kung, C.H. (1991). Formal Definition and
Verification of Data Flow Diagrams. Journal of Systems and Software, 16(1), pp.
29-36.
[6] Tong, L. and Tang, C.S. (1991). Semantic Specification
and Verification of Data Flow Diagrams. Journal of Computer Science and
Technology, 6(1), pp. 21-31.
[7] Lee, P.T and Tan, K.P. (1992). Modelling of visualized
data-flow diagrams using Petri Net Model. Software Engineering Journal, January
1992, pp. 4-12.
[8] Kim, D.H. and Chong, K. (1996). A Method of Checking
Errors and Consistency in the Process of ObjectOriented Analysis. Proceedings
of the 1996 Third Asia-Pacific Software Engineering Conference. Korea: IEEE
Computer Society. Pp. 208-216.
[9] Rosziati Ibrahim and Noraini Ibrahim. A Tool for Checking
Conformance of UML Specification. Proceedings of the 2009 World Academic of
Science and Technology (WASET), Volume 51 (v51-45), pp262-266.
[10] Leavens, G.T., Wahls, T. and Bakar, A.L. (1999). Formal
Semantics for SA Style Data Flow Diagram Specification Languages. Proceedings
of the 1999 ACM Symposium on Applied Computing. Oregon, US: IEEE Computer
Society. pp. 526–532.
[11] Jeffrey, A. H., George, J.F. and Valacich, J.S. (2002)
Modern Systems Analysis and Design. 3rd ed. US: Prentice-Hall.
[12] Donald, S. and Le Vie, Jr. (2000). Understanding Data
Flow Diagram. Proceedings of the 47th annual conference on Society for
Technical Communication. Texas: Integrated Concepts, Inc.
Citation
Count –19
SOFTWARE METRICS
VALIDATION METHODOLOGIES IN SOFTWARE ENGINEERING
K.P. Srinivasan1 and T. Devi2
1Associate Professor in Computer Science,C.B.M. College, Kovaipudur,
Coimbatore 641 042, India
2Professor and Head,
Department of Computer Applications,School of ComputerScience
and
Engineering,Bharathiar University, Coimbatore – 641 046, India.
ABSTRACT
In the software measurement validations, assessing the validation
of software metrics in software engineering is a very difficult task due to
lack of theoretical methodology and empirical methodology [41, 44, 45]. During
recent years, there have been a number of researchers addressing the issue of
validating software metrics. At present, software metrics are validated
theoretically using properties of measures. Further, software measurement plays
an important role in understanding and controlling software development
practices and products. The major requirement in software measurement is that
the measures must represent accurately those attributes they purport to
quantify and validation is critical to the success of software measurement.
Normally, validation is a collection of analysis and testing activities across
the full life cycle and complements the efforts of other quality engineering
functions and validation is a critical task in any engineering project.
Further, validation objective is to discover defects in a system and assess
whether or not the system is useful and usable in operational situation. In the
case of software engineering, validation is one of the software engineering
disciplines that help build quality into software. The major objective of
software validation process is to determine that the software performs its
intended functions correctly and provides information about its quality and
reliability. This paper discusses the validation methodology, techniques and
different properties of measures that are used for software metrics validation.
In most cases, theoretical and empirical validations are conducted for software
metrics validations in software engineering [1-50].
KEYWORDS
Result Based Software Metrics (RBSM), Software Metrics
Validations, Theoretical Validations, EmpiricalValidations, Software
Measurement, Object-Oriented Metrics, Software Engineering
REFERENCES
[1] Aggarwal, K.K., Singh, Y., Kaur, A., and Malhotra, R.,
2006, “Software Design Metrics for ObjectOriented Software,” Journal of Object Technology, Vol. 6,
No. 1, January-February, pp. 121-138.
[2] Alshayeb, M., and Li, W., 2003, “An
Empirical Validation of Object-Oriented Metrics in Two Different Iterative
Software Processes,” IEEE
Transactions on Software Engineering, Vol. 29, No. 11,November, pp. 1043-1049.
[3] Anbumani, K., and Srinivasan, K.P., 2005, “A Set of Object-Oriented Design Metrics,” Journal of The Institution of Engineers
(India), IE(I), Journal – CP, Volume 86, May, pp. 1-9.
[4] Archer C. and Stinson M.,1995, Object-Oriented Software
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Citation
Count – 19
A NEW APPROACH TO REQUIREMENT ELICITATION BASED
ON STAKEHOLDER RECOMMENDATION AND COLLABORATIVE FILTERING
Nilofar Mulla1 and Sheetal Girase2
1Department of Information Technology, MIT Pune 38, Maharashtra,
India
2Asst.Prof. Department
of Information Technology, MIT Pune 38, Maharashtra,India
ABSTRACT
The customers' needs in a software project are identified in
the process of Software requirements elicitation. For building a software
system this process is considered as one of the most important parts. In this
part it is decided precisely what will be built. A close interaction between
developers and end-users of the system is needed by requirements’ gathering.
Meetings can be costly, inconvenient and infrequent if developers and end-users
are in different organizations or different cities. The quality of the elicited
requirements can greatly be impacted if there is a problem of communication.
Requirement elicitation is a process difficult to scale to large software
projects with many stakeholders which involves identifying and prioritizing
requirements. A stakeholder is an individual or a group who can influence or be
influenced by the success or failure of a project. Existing methods to identify
and prioritize requirements do not scale well to large projects. Large projects
tend to be beset by three problems: information overload, inadequate
stakeholder input, and biased prioritization of requirements. Existing methods
to identify and prioritize requirements do not scale well to large projects.
Existing requirements prioritization methods require substantial efforts from
the requirements engineers when there are many requirements. To address the
problems Stakeholder recommender model will contain steps:-Identify the large
project, Analysis of requirements, Identify and prioritize stakeholders,
Predict requirements, Prioritize requirements. For making predictions, our
approach will use one of the most well known algorithms that is k-Nearest
Neighbor (kNN) algorithm. KNN is used to identify like-minded users with
similar rating histories in order to predict ratings for unobserved users-item
pairs. A unique subset of the community for each user is found out by KNN by
identifying those with similar interests. To do so, every pair of user profile
is compared to measure the degree of similarity. A neighbourhood is created for
each user by selecting the k most similar users. The similarity between each
pair of user profiles for users in the neighbourhood is used to compute
predicted ratings.
KEYWORDS
Requirements
Elicitation, Stakeholder, k-Nearest Neighbour.
REFERENCES
[1] Soo Ling Lim, and Anthony Finkelstein. “StakeRare:
Using Social Networks and Collaborative Filtering for Large-Scale Requirements
Elicitation”, IEEE Transactions On Software Engineering 2011
[2] Lim, S.L., D. Quercia, and A. Finkelstein. “StakeNet: using social
networks to analyze the stakeholders of large-scale software projects”, in
Proceedings of the 32nd ACM/IEEE International Conference on Software
Engineering-Volume 1. 2010.
[3] Castro-Herrera, C. J. Cleland-Huang, and B. Mobasher
Enhancing stakeholder profile to improve recommendation in online requirements
elicitation Proceedings of the 17th IEEE International Conference on
Requirements Engineering. 2009: IEEE Computer Society. p. 37-46.
[4] Lim, S.L., “Social
Networks and Collaborative Filtering for Large-Scale Requirements Elicitation.
PhD thesis, 2010,University of New South Wales.”
[5] Herrmann, A., M. Danev Requirement prioritization based
on benefit and cost prediction- an agenda for future research Proceedings 16th
IEEE International Conference on Requirement Engineering 2008.
[6] Azar J, R.K. Smith, D. Cordes, Value oriented
requirements prioritization in small development organization,IEEE Software
2007
[7] Davis, A., O. Dieste, A. Hickey, N. Juristo, and A.M.
Moreno. “Effectiveness of requirements elicitation techniques:
Empirical results derived from a systematic review”, in Proceedings of the 14th IEEE
International Conference on Requirements Engineering. 2006. p.
[8] Charette. Why software fails, IEEE Spectrum, 2005
[9] Alexander, I. and S. Robertson, Understanding project
sociology by modeling stakeholders. IEEE Software, 2004. 21(1).
[10] Zhang, Q., Nishimura, T., “A
Method of Evaluation for Scaling in the Analytical Hierarchy Process”, Proceedings of the IEEE International
Conference on Systems, Man and Cybernetics, Vol. 3, pp. 1888-1893, 1996.
[11] D. Leffingwell and D. Widrig, Managing Software
Requirements – A Unified Approach, Addison Wesley, 2003.
BAYESIAN NETWORK BASED XP PROCESS MODELLING
Mohamed Abouelela, Luigi Benedicenti
Software System
Engineering, University of Regina, Regina, Canada
ABSTRACT
A Bayesian Network based mathematical model has been used for
modelling Extreme Programming software development process. The model is
capable of predicting the expected finish time and the expected defect rate for
each XP release. Therefore, it can be used to determine the success/failure of
any XP Project. The model takes into account the effect of three XP practices,
namely: Pair Programming, Test Driven Development and Onsite Customer
practices. The model’s predictions were validated against two case studies.
Results show the precision of our model especially in predicting the project
finish time.
KEYWORDS
Bayesian Networks,
Extreme Programming, Process Modelling, Software Process.
REFERENCES
[2] S. Kuppuswami, K. Vivekanandan, Prakash Ramaswamy, and
Paul Rodrigues. “The effects of individual xp practices on software
development effort”. SIGSOFT Softw.
Eng. Notes, 28(6):6–6, 2003.
[3] P. Hearty, N. Fenton, D. Marquez, and M. Neil. “Predicting
Project Velocity in XP Using a Learning Dynamic Bayesian Network Model”. Ieee Transactions On Software
Engineering, Vol. 35, No. 1, January/February 2009.
[4] M. Melis “A
Software Process Simulation Model of Extreme Programming” PhD thesis. February 7, 2006.
[5] N. E. Fenton and S. Pfleeger. “Software Metrics: a rigorous and pratical approach”. PWS Publishing Company, 1996.
[6] V. B. Misic, H. Gevaert, and M. Rennie. “Extreme
dynamics: Modeling the extreme programming software development process.” In Proceedings of ProSim04 workshop on
Software Process Simulation and Modeling, pages 237–242, 2004.
[7] A. Cockburn and L. Williams. “The
costs and benefits of pair programming.” In Proceedings of the First International Conference
onExtreme Programming and Flexible Processes in Software Engineering (XP2000),
Cagliari, Sardinia, Italy, June 2000.
[8] Jerzy Nawrocki and Adam Wojciechowski. “Experimental evaluation of pair programming.” In 12th European Software Control and
Metrics Conference (ESCOM 2001), 2001.
[9] K. Vivekanandan. “The Effects of Extreme
Programming on Productivity, Cost of Change and Learning Efficiency.” PhD thesis, Department of Computer
Science, Ramanaujam School of Mathematics and Computer Sciences, Pondicherry
University, India., 2004.
[10] Hanna Hulkko and Pekka Abrahamsson.”A multiple case study on
the impact of pair programming on product quality.” In ICSE ’05:
Proceedings of the 27th international conference on Software engineering, pages
495–504, New York, NY, USA, 2005. ACM Press.
[11] Randy A. Ynchausti. “Integrating unit testing into a
software development teams process.” Published on: http://www.agilealliance.org/articles/ynchaustirandyaintegr/file,
2001.
[12] Boby George and Laurie Williams. “An initial investigation of test driven development in
industry.” In Proceedings
of the 2003 ACM symposium on Applied computing, pages 1135–1139. ACM Press,
2003.
[13] S. Kuppuswami , K. Vivekanandan , Prakash Ramaswamy ,
Paul Rodrigues, “The effects of individual XP practices on software
development effort”, ACM SIGSOFT
Software Engineering Notes, v.28 n.6, November 2003.
[14] M. Korkala, P. Abrahamsson, and P. Kyllo¨nen, “A Case Study on the Impact of Customer Communication on
Defects in Agile Software Development,” Proc. AGILE Conf., 2006.
[15] Ben-Gal I., Ruggeri F., Faltin F. & Kenett R., Bayesian
Networks, in Encyclopedia of Statistics in Quality & Reliability, Wiley
& Sons, 2007
[16] Norman Fenton and Martin Neil, “Managing
Risk in the Modern World: Applications of Bayesian Networks” Knowledge transfer report, London
Mathematical Society, 2007.
[17] P. Abrahamsson, "Extreme Programming: First Results from a Controlled Case
Study," presented
at 29th. IEEE EUROMICRO Conference, Belek, Turkey, 2003.
Citation
Count –18
A COMPARISON OF PARAMETER BEST ESTIMATION
METHOD FOR SOFTWARE RELIABILITY MODELS
Latha Shanmugam1and Dr. Lilly Florence2
1Research Scholar, Anna University, Tamil Nadu
2Professor, Adiyamaan
College of Engineering, Hosur.
ABSTRACT
During the past few Decades, many software reliability growth
models have been suggested for estimating reliability of software as software
reliability growth models. The Functions suggested were non-linear in nature,
so it was difficult to estimate the proper parameters. An Estimation method
based on Ant Colony Algorithm in which parameters are estimated is discussed in
this paper.In this paper, Numerical examples which have been based on five sets
of real failure data have been discussed Using existing methods viable
solutions for some of the models and data sets cannot be obtained, where as in
the proposed method, at least one solution can be obtained. The accuracy of the
results using proposed method when compared with PSO algorithm has higher
accuracy for at least 10 times for majority of the models.
KEYWORDS
Software
Reliability Growth Model, Estimation, Particle Swam Optimization, Ant Colony
Algorithm.
Volume Link :http://www.airccse.org/journal/ijsea/vol3.html
REFERENCES
[1] New Paradigm for Software Reliability Estimation by
Ritika Wason, P. Ahmed, M. Qasim Rafiq in International Journal of Computer
Applications (0975-887) vol 44-No14. April 2012.
[2] Software Reliability: Models and Parameters Estimation by
Vladimir Zeljikovic, Nela Radovanovic, Dragomir Ilic, Scientific Technical
Review, 2011, Vol.61, No.2, pp. 57 -60.
[3] Estimation of parameters of the Gompertz distribution
using the least squares method by Jong-Wuu Wu, Wen-Liang Hung, Chi-Hui Tsai in
Applied Mathematics and Computation (2004) 133-147, available @ 2003 Elsevier.
[4] T.Ando, H. Okamura, T.Dohi, Estimating Markov Modulated
Software Reliability Models via EM Algorithm[C]. Proceedings of the 2nd IEEE
International Symposium on Dependable, Autonomic and Secure Computing. 2006.
[5] T.Minohara and Y.Tohma, Parameter Estimation of
Hyper-Geometric Distribution Software Reliability Growth Model by Genetic
Algorithms[C], Proceedings of the 6th IEEE International Symposium on Software
Reliability Engineering (ISSRE 1995), pp.324-329, Toulouse, France, 1995.
[6] A Parameter Estimation Method for Software Reliability
Models, Changuou Zheng, Xiaoming Liu, Song Huang and Yi Yao in Advanced in
Control Engineering and Information Science, available SciVerse Science Direct
Procedia Engineering 2011.
[7] ZHANG Ke-han, LI Ai-guo, SONG Bao-wei. Estimating
Parameters of Software Reliability models using PSO. Computer Engineering and
Applications, 2008, 44(11):47-49.
[8] What is Hampering the performance of Software Reliability
Models? A Literature review in Proceedings of the International Multi
Conference of Engineers and Computer Scientists 2009 Vol 1, IMECS 2009.
[9] Software Reliability Growth Modeling: Models and
Applications by Shigeru Yamada and Shunji Osaki. IEEE Transactions on Software
Engineering, Vol, SE-11, No.12 December 1985.
[10] A Coloni, M Dorigo and V Maniezzo. Ant system:
Optimization by a colony of cooperating agent [J]. IEEE Trans. Systems Man and
Cybemetics-Part B: Cybemetics. 1996, 26(1):29-41 [7]
[11] Lyu M R. Handbook of software reliability engineering
[M]. New York: Mc Graw-Hill and IEEE Computer Society Press, 1996.
[12] Particle Swam Optimization: Techniques System and
challengeous. By DianPalupiRini, Siti Mariyam, Shamsuddin, Siti Sophyyati
Yuhaniz. International Journal of Computer Applications (0975-8887) Vol 14- No.
1 Jan 2011.
[13]Macro Dorigo, Luca M. Gambardella Mauro Biratlari,
Alcherio Martino, Riceardo Poli and Thomas stutze, editors, LNCS 4150, Ant
Colony Optimization and swam intelligence, 5th International workshop, ANTs
2006 pages 72-83, Berlin, Germany 2006, Springer – Verlag.
[14]R. Jiang “Required Characteristics for Software
Reliability Growth Models” in World Congress on Software Engineering IEEE 2009.
DOI 10.1109/WCSE.2009.157
Citation
Count – 27
Survey of maintenance
policies for the Last 50 Years
Asis Sarkar#1 Dr Subhash Chandra Panja# 2
and Dr Bijan Sarkar # 3
1Department of mechanical Engineering
N.I.T.Agartala, P.O.:-
t.e.c, Barjala, Agartala: - 799055,(India)
2,3 Department of mechanical Engineering
P.O.—Jadavpur University
Calcutta:--700032,(India)
ABSTRACT
In the past several decades, maintenance and replacement
problems have been extensively studied in the literature. Thousands of
maintenance and replacement models have been created. However, all these models
can fall into some categories of maintenance policies: age replacement policy,
block replacement policy, periodic preventive maintenance policy, failure limit
policy, sequential preventive maintenance policy, repair cost limit policy,
repair time limit policy, repair number counting policy, reference time policy,
mixed age policy, group maintenance policy, opportunistic maintenance policy, etc.
Each kind of policy has different characteristics, advantages and disadvantages
with lot of contributions from Research scientist, Technologists... This survey
summarizes, classifies, and compares various existing maintenance policies
Around 170 Authors and their research works are presented in the Reference
section. It will help to look into the different policies which is appropriate
to the organization and for further study the reference section will be helpful
for the researchers for further knowledge.
KEYWORDS
Maintenance policy;
Maintenance; Reliability; Replacement; Optimization.
Volume Link : http://www.airccse.org/journal/ijsea/vol2.html
REFERENCES
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York; 1996.pp 11-45
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Citation
Count – 18
OPEN STANDARDS AND OPEN
SOURCE: ENABLING INTEROPERABILITY
Fernando Almeida1 , José Oliveira2and José Cruz3
1Innovation and Development Center, ISPGaya, V.N.Gaia, Portugal
2Faculty of Economics
and INESC Porto, University of Porto, Porto, Portugal
3Faculty of Engineering,
University of Porto, Porto, Portugal
ABSTRACT
Interoperability is a major requirement for industries and
governments in a society that increasingly moves towards global collaboration
and integration. Open standards built on the principles of openness,
transparency and consensus lay the grounds for innovation, growth and fair
competition. Open standards are not synonymous of open source. The former is a
set of specifications, the latter is an implementation.However, they share
their commitment to openness and defend the equal opportunities of everyone to
participate. This paper looks to the open source as the best way to enable
interoperability between different technologies and applications. The role of
open standards in interoperability is analyzed and some of the policies introduced
by the European Union for the use and dissemination inside Members States are
examined. Additionally, the use of open source software combined with open
standards is presented and its major social benefits and economic impacts are
highlighted.
KEYWORDS
Open Standards,
Open Source, Interoperability, Software Development.
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