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The society and individuals who acquire skills and increase their technological information are able to analyze the data generated and acquire information of digital environment to enter into a higher economic stratum. The one who enhance the skills are vibrant and inspirational in society while those who remain illiterate digitally are being pushed down the ladder and have to invest hard in terms of money and knowledge for their successive and progressive growth. To get along with the advancement and enhancement of technology the political system has to adjust itself and proceed in tandem dynamically. The adaption of encouraged technological advancement in the field of computer systems and relative areas has given immense impetus to economical growth of company. Digital India programme is an effort in this idea. The presented work reviews the programme and various issues that have addressed along with the implementation and challenges.

Digital India;Digital India opportunities; Digital India challneges

[1] G. Eason, B. Noble, and I.N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955. (references)
[2] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
[3] I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350.
[4] K. Elissa, “Title of paper if known,” unpublished.
[5] R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
[6] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
[7] M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.

Payal Thakur, Kalpana Rangra , "Digital India : Opportunities and Challenges", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.1-4, 2016.

Test data generation is a key problem in software testing. Many automatic tools are already present but some are not optimal for large scale, some requires information of local or global solution of problem, some are not suitable to run time conditions. In this paper simple GA & hybrid GA have been implemented to produce automatic data set for testing under basis path testing criteria using branch distance based fitness function in MATLAB. Experimental comparison has been performed first up to twenty five iterations and second up to fifty iterations on same initial population set & then on randomly generated initial population set. After these comparisons conclusion has been made.

Basis path coverage testing, Branch distance fitness function, Simple genetic algorithm, Hill climbing, Memetic genetic algorithm.

[1] B. Antonia. “Software Testing Research: Achievements, Challenges and Dreams”.Future of Software Engineering, IEEE Computer Society, (2007): 85-103.
[2] B. W. Kernighan and P. J. Plauger.“The Elements of Programming Style”.McGraw-Hill, Inc.New York, NY, USA (1982).
[3] M. Alzabidi, A. Kumar and A. D. Shaligram. “Automatic Software Structure Testing by Using Evolutionary Algorithms for Test Data Generations”.IJCSNS: International Journal of Computer Science and Network Security on 9, no. 4 (2009).
[4] D. Garg and P. Garg.“Comparison of BDBFF & ALBFF for Basis Path Testing Using GA”.International Journal of Advanced Research in Computer Science and Software Engineering on 5, no. 7 (2015).
[5] T. K. Wijayasiriwardhane, P. G. Wijayarathna and D. D. Karunarathna.“An Automated Tool to Generate Test Cases for Performing Basis Path Testing”.Proc. International Conference on Advances in ICT for Emerging Regions, IEEE Computer Society (2011): 95-101.
[6] G. L. Latiu, O. A. Cret and L. Vacariu. “Automatic Test Data Generation for Software Path Testing using Evolutionary Algorithms”.Proc. Third International Conference on Emerging Intelligent Data and Web Technologies, IEEE Computer Society (2012): 1-8.
[7] G. M. C. Michael and M. Schatz. "Generating software test data by evolution".IEEE Transactions on Software Engineering on 27 (2001):1085-1110.
[8] W. Joachim and S. Harmen. “Suitability of Evolutionary Algorithms for Evolutionary Testing”.Proc. of the 26th Conf. on Prolonging Software Life: Development and Redevelopment, IEEE Computer Society (2002).
[9] D. E. Goldberg. “Genetic Algorithms in Search Optimization and Machine Learning”.Addison Wesley Longman, Inc., ISBN 0-201- 15767-5 (1989).
[10] N. Singh and K. Aggarwal.“Software Testing using Evolutionary approach”.International Journal of Scientific and Research Publications on 3, no. 6 (2013).
[11] J. Holland. “Adaptation in Natural and Artificial Systems”.University of Michigan Press (1975).
[12] P. Mascato and P. C. Cotta.“A gentle introduction to memetic algorithms”.handbook of Metahuristics (2003):105-144.
[13] D. Garg and P. Garg. “Basis Path Testing Using SGA & HGA with ExLB Fitness Function”. Elsevier Procedia Computer Science on 70 (2015): 593-602.
[14] B. Korel. "Automated software test data generation". IEEE Transactions on Software Engineering on 16 (1990): 870-879.

Deepak Garg, Pardeep Kumar, "Comparative Study of Simple GA & Hybrid GA for Basis Path Testing under Branch Distance Fitness Function", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.5-10, 2016.

Embedded Systems require combination of Software Electrical and Mechanical Engineering. This leads to increased complexity of Embedded Systems with shorter production cycle. These evolutions pose new challenges to traditional embedded system testing approaches, because besides time constraints, embedded system products are expected to meet quality constraints prior to their deployment in field. The Cost and Time of Testing Embedded Systems consumes a considerable portion of entire development cycle. With Increased Complexity of Embedded Software it becomes imperative that our Functional Testing is effective at detecting issues in the system. With the above requirements we propose to use Model Based Testing (MBT) for the functional testing of Embedded Systems during the manufacturing process in the production line. The comprehensive and systematic approach of MBT facilitates automated testing with reduced test time and cost along with improved quality of the product.

Embedded system, Model Based Testing, Functional Testing, System Testing

[1] OMG: http://www.omg.org [28/04/16].
[2] Mark Utting and Bruno Legeard, “Practical Model-Based Testing: A Tools Approach". Elsevier, 2006.
[3] Muhammad Shafique, Yvan Labiche, “A Systematic Review of Model Based Testing Tool
[4] Support,” International Journal on Software Tools for Technology Transfer February 2015, Volume 17, Issue 1, pp 59-76.
[5] Yasir Dawood Salman, Nor Laily Hashim, “An Improved method of obtaining basic path testing for test case based on UML State Chart,” International Symposium on Research in Innovation and Sustainability 2014 (ISoRIS ’14)
[6] B. Beizer, “Software Testing Techniques”, Second Edition, 1990.
[7] C. Bourhfir, R. Dssouli, E. Aboulhamid, N. Rico, “Automatic executable test case generation for extended finite state machine protocols,” Testing of Communicating Systems Part of the series IFIP — The International Federation for Information Processing pp 75-90.
[8] Fuqing Wang, Shuai Wang, Yindong Ji, “An Automatic Generation Method of Executable Test Case Using Model Driven Architecture ,” The 4th International Conference on Innovative Computing, Information and Control (ICICIC), 2009.
[9] Fabrice Ambert, Fabrice Bouquet, Jonathan Lasalle, Bruno Legeard and Fabien Peureux, “Applying an MBT Toolchain to Automotive Embedded Systems: Case Study Reports,” The Fourth International Conference on Advances in System Testing and Validation Lifecycle (VALID) 2012.
[10] Jonathan Lasalle, Fabien Peureux, Frédéric Fondement, “Development of an automated MBT toolchain from UML/SysML models,” SI : FM & UML Innovations in Systems and Software Engineering December 2011, Volume 7, Issue 4, pp 247-256
[11] Lionel Briand, Yvan Labiche, “A UML-Based Approach to System Testing,” UML 2001 — The Unified Modeling Language. Modeling Languages, Concepts, and Tools Volume 2185 of the series Lecture Notes in Computer Science pp 194-208.
[12] L. C. Briand; M. Di Penta; Y. Labiche, “Assessing and improving state-based class testing: a series of experiments,” IEEE Transactions on Software Engineering 2004, Volume: 30, Issue: 11 Pages: 770 - 783.
[13] Sang-Uk Jeon, Jang-Eui Hong, Doo-Hwan Bae, “Interaction-based Behavior Modeling of Embedded Software using UML 2.0,” Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC'06).
[14] Martin Grant, Lavagno Luciano, Jean Louis-Guerin, “Embedded UML: a merger of real-time UML and co-design,” Springer 2003.
[15] Helmerich, A., Koch, N. and Mandel, L., Braun, P., Dornbusch, P., Gruler, A., Keil, P., Leisibach, R., Romberg, J., Schätz, B., Wild, T. Wimmel, G.: “Study of Worldwide Trends and R&D Programmes in Embedded Systems in View of Maximising the Impact of a Technology Platform in the Area,” Final Report for the European Commission, Brussels Belgium, 2005.
[16] Mohamed Mussa, Samir Ouchani, Waseem Al Sammane, Abdelwahab Hamou-Lhadj, “A Survey of Model-Driven Testing Techniques,” Ninth International Conference on Quality Software 2009, Pages: 167 - 172.

Mohammed Naim Khan, Namita Arya and Amit Prakash Singh, "MBT for Functional Testing of Embedded Systems", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.10-16, 2016.

Big data have emerged very fast in the last couple of years and it came up with a number of solutions to different problems varies from effective decision making, real time decision making, and effective implementation of E-Governance etc. Information and Communication technology is the major responsible for this boom in the data. It has also given birth to number of challenges like Effective data architectures, Effective analysis algorithm, Frameworks to handle this big data. As the size of data is increasing repeatedly, it gives a challenge of scaling the frameworks which could be able to handle such data. On the basis of aforesaid problems and challenges this paper has proposed a scalable architecture for the analysis of Big Data in higher Education System.

Data, ICT, Scalability, Frameworks, Architectures, Education system

[1] A Munar, E. Chmer and I. Sales, “A Big Data Financial Information Management Architecture for Global Banking”, IEEE International Conference on Future Internet of Things and Cloud, pp: 385-388, 2014.
[2] S.Marshal, X. Jiang, R. State and T. Engel, “A Big Data Architecture for large Scale Security Monitoring”, IEEE International Congress on Big Data, pp: 56-63, 2014.
[3] N. Agnihotri and A.K.Sharma, “Evaluating PaaS Scalability And Improving Performance Using Scalability Improvement Systems”, International Journal of Research in Engineering and Technology, pp: 598-602, 2014.
[4] E.Moguel, J.C. Preciado, F.S. Figueroa, Miguel A. Preciado and Juan Hernandez, “ Multilayer Big Data Architecture for Remote Sensing in Eolic Parks”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015.
[5] L.Ramaswamy, V. Lawson and S.V. Gogineni, “Towards A Quality-Centric Big Data Architecture for Federated Sensor Services”, IEEE International Congress on Big Data, pp: 86-93, 2013.
[6] S. Asur and B. A. Huberman, “Predicting the future with Social Media”, IEEE/WIC/ACM, International conference on web intelligent agent technology, pp. 492-499, 2010,
[7] N. Agnihotri and A.K. Sharma, “Big data analysis and its need for effective E-governance”, International Journal of Innovations & Advancement in Computer Science, Vol. 4, pp. 219-224, 2015.
[8] H. Chen, R. H. L. Chiang and V. C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact” MIS Quarterly, pp 1165–1188, 2012.
[9] Official website of SAS company http://www.sas.com/big-data/
[10] Official webpage of IBM company: http://www-01.ibm.com/software/data/bigdata/
[11] D. Stodder,” Applying analytics with big data for customer intelligence”, TDWI checklist report, online : www.tdwi.org.

Nishant Agnihotri, Aman Kumar Sharma, "Proposed Scalable Architecture for Analyzing Big Data in Education System", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.17-21, 2016.

The world of software development is moving from monoliths to micro-service architecture. Instead of having one big application, handling all tasks, there is a paradigm shift towards creating many small applications doing just a unit of work. When we build software-as-a-service we have to look into development, maintenance, and scalability of the application. Over the time it has been observed that the post development tasks of an application are inhibited because application is not just doing one task that it is supposed to do but all the activities that can be delegated. With twelve-factor pattern we can come close to create micro-services that can reduce cost of development, have maximum portability when deploying on different environment, are cloud ready and most importantly scale up. Spring.io provide a rich underlying framework, which can help in creating applications that are twelve-factor compliance, with Spring Boot they took it to a whole new level. With twelve-factor methodology in mind, in this research paper we will try to look into how spring can help covering each point in twelve-factors.

Twelve-factor application; Cloud native; Spring-framework; Micro-services

[1] S. Newman, “Building Microservices”. O’Reilly, 2015
[2] V.Andrikopoulos, S.Strauch,C. Fehling and F.Leymann, “CAP-Oriented Design for Cloud-native Applications”
[3] S.Otte, “Version Control Systems.” Computer Systems and Telematics, Institute of Computer Science, FreieUniversität, Berlin, Germany2009.
[4] N.Sangal, E. Jordan, V.Sinha, and D..Jackson “Using dependency models to manage complex software architecture” in Proceedings of 20thAnnual ACM Conf. Object-Oriented Programming, Systems, Languages, New York, 2005
[5] J.Salecker, and D.Schütz, “Bill of Material” in Proceedings of 9thEuropean Conference on Pattern Languages of Programs (EuroPLoP 2004). UVK, Konstanz, Germany 2005
[6] Y. Tao, Y. Dang, T.Xie, D. Zhang, and S. Kim, “How do software engineers understand code changes? An exploratory study in industry” in Proceeding of ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering, Cary, North Carolina, November 11-16, 2012
[7] Maurer, Michael, I.Brandic, and R.Sakellariou, "Adaptive resource configuration for Cloud infrastructure management." Future Generation Computer Systems 29, no. 2, 2013,pp.472-487.
[8] D. Bernstein, "Containers and Cloud: From LXC to Docker to Kubernetes", IEEE Cloud Computing, vol.1, no. 3, Sept. 2014, pp.81-84
[9] M.Hogan, "Shared-Disk vs. Shared-Nothing" - http://www.sersc.org/journals/IJEIC/vol2_Is4/15.pdf
[10] Tanenbaum, S. Andrew, V. Steen, and Maarten, “Distributed Systems: Principles and Paradigms” Prentice Hall ISBN 0-13-088893-1
[11] M. Stine,“Migrating to Cloud-Native Application Architectures,”O’Reilly, 2015
[12] A Modernized Software Environment for Developing, Deploying and Operating Cloud Applications, vmware.http://www.vmware.com/files/pdf/Modernizing_App_Develpment_Whitepaper.pdf
[13] J.Cito, P.Leitner,, H. C. Gall, A. Dadashi, A. Keller, and A Roth, “Runtime Metric meets Developer - Building better Cloud Applications using Feedback,” in Proceedings of the 2015 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming & Software (Onward! 2015), 2015.

Dinkar Thakur, Arvind Kalia, "Twelve-Factor application pattern with Spring Framework", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.22-37, 2016.

Open source software (OSS) projects are available in various application domains in a large number. It is difficult to choose any product/project. Quality is a big concern while choosing a software among many of the same type. To measure the quality there are many quality models around us since long. More specifically, from 2003 ,there has been many quality models available in OSS. But how good these quality models are ,remains the question of concern. This research is intended to provide an insight in existing quality models and provide the strength & weakness of these models, thus to provide the OSS community a best suitable quality model. In this paper, the researchers have reviewed the available literature on some conventional quality models and some selective OSS quality models, and carried out a comparative study on these models. In first generation models, open source maturity model embraces the maximum quality characteristics. We concluded that Qual-OSS quality model in second generation is close to international standard organization (ISO) 25010 quality standard. The second generation models offer more tools to support the quality evaluation by considering the community characteristic of OSS.

OSS, OSMM, ISO, Qual-OSS, Qualipso, OpenBRR, QSOS, QualOSS, CMM,OSSD

[1] Miguel J.P., Mauricio D., and Rodríguez G., “A Review of Software Quality Models for the Evaluation of Software Products”, International Journal of Software Engineering & Applications, Vol.5 Issue 6, 2014, pp. 31-53.
[2] Haaland.K, Groven A.K, Regnesentral.N,Glott R,Tannenberg A., “Free/Libre Open Source Quality Models-A comparison between two approaches”, In 4th FLOS International Workshop on Free/Libre/Open Source Software, 2010, pp. 1-17.
[3] Adewumi, Misra S., and Omoregbe N., “A Review of Models for Evaluating Quality in Open Source Software,” 2013 International Conference on Electronic Engineering and Computer Science, IERI Procedia, vol. 4, 2013, pp. 88 – 92.
[4] http://www.sqa.net/iso9126.html ,accessed on May 8,2016.
[5] Adewole A, Misra S., Omoregbe N., “Evaluating Open Source Software Quality Models against ISO 25010 , 2015 IEEE International Conference on Computer and Information Technology”, Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing- Pervasive Intelligence and Computing ,pp 872-877.
[6] Garvin, D. A., "What does 'Product Quality' really mean?", Sloan Management Review, no. 1, pp. 25-43, 1984.
[7] ISO/IEC 25010, “Systems and Software Engineering—Systems and Software Product Quality Requirements and Evaluation (SQuaRE)— System and Software Quality Models,” International Organization for Standardization, Geneva, 2010.
[8] Robson, C., “Real world research: a resource for social scientists and practitioner-researchers”, Blackwell Publisher Ltd., 2002.
[9] McCall, J. A., Richards, P. K., and Walters, G. F., "Factors in Software Quality", Nat'l Tech.Information Service, Vol. 1, 2, and 3, 1977.
[10] Boehm Barry W., Brown, J. R, and Lipow, M.,” Quantitative evaluation of software quality, International Conference on Software Engineering”, Proceedings of the 2nd international conference on Software engineering, 1976.
[11] Boehm, B. W., Brown, J. R., Kaspar, H., Lipow, M., McLeod, G., and Merritt, M.,“Characteristics of Software Quality”, North Holland, 1978.
[12] ISO, International Organization for Standardization, "ISO 9126-1:2001, Software engineering – Product quality, Part 1: Quality model", 2001.
[13] Wijnen - Meijer, M. “Detailed comparison of existing open source software evaluation models”, OSOSS, 2006 http://noiv.nl/files/2009/12/models_comparison_-_1_5.pdf
[14] Wilson, J., “Open Source Maturity Model” 2006,http://www.osswatch.ac.uk/resources/osmm.xml, section 3.
[15] “OSS Watch at the University of Oxford also considers methods like OpenBBR to be useful”,http://www.osswatch.ac.uk/resources/brr.xml, section 4.
[16] https://en.wikipedia.org/wiki/OpenSource_Maturity_Model ,assessed on May 9,2016
[17] M. Soto, and M. Ciolkowski, “The QualOSS open source assessment model measuring the performance of open source communities”. In Proceedings of the 3rd ESEM, 2009, pp. 498–501.
[18] I. Samoladas, G. Gousios, D. Spinellis, and I. Stamelos, “The SQO-OSS quality model: measurement based open source software evaluation”. In Open source development, communities and quality, Springer, 2008, pp. 237-248. .
[19] Bari M. and Djouab R., “Software quality and e-learning,” presented at International Conference on e-Education, Mostar, Bosnia and Herzegovina, September 26-27, 2014
[20] Sung W.J.,Kim Hyeok Ji,Rhew Y. Sung,”A quality Model for open source selection”, Sixth international conference on advanced language processing and web information tecgnology,2007,pg515-518,2007
[21] G.Lott R.,Groven,Haaland K.,Tannenberg,”Quality models for free /libre open source software –towards the ‘silver bullet’?”,2010 36th EUROMICRO conference on software engineering and advanced applications,pg 439-446
[22] Aberdour Mark,”Acheivng quality in open source software ”,IEEE SOFTWARE published by the ieee computer society, Jan 2007.Pg 58-64.

Harvinder Chauhan ,Anita Ganpati, Hardeep Singh, "A Review of Quality Models for Evaluating Quality in Open Source Software", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.38-42, 2016.

The database of information system of any organization store crucial information. Organizations are considering this stored information as asset, because the information provided by the organization's information system play an important role in its functioning and also in the decision making processes. Depending on the availability of quality of information, administrative authorities of organization can take right decision for its stakeholders and for its better functioning. Keeping this in mind, present paper analysed the database of the University Examination System of Himachal Pradesh University. Data stored in the database of examination system is analysed and number of discrepancies are revealed in the data. There is lot of redundant and inconsistent data stored in the examination database. Upon analysing the data, it was found that there are number of fields in the database that store the redundant data, significant number fields are found empty besides, few constraints has been enforced on the majority of data. Poor database design is one of many factor that contributes to such problems. This paper also analyses the structure of relational table that lead to database of University Examination system and in the last highlight the findings of analysis.

Database, Data Quality, Information System, University Examination System

[1] Judd, Charles and, McCleland, Gary. ''Data Analysis''. Harcourt Brace Jovanovich. ISBN 0-15-516765-0. 1989
[2] O'Neil, Cathy and, Schutt, Rachel. ''Doing Data Science''. O'Reilly. ISBN 978-1-449-35865-5. 2
[3] R. K. Yin . Case Study Research, Design and Methods, 3rd ed. Newbury Park, Sage Publications, 2002.
[4] Huang, Huan-Tsae, Lee, Y. W. and Wang, R. Y. Quality information and knowledge, Prentice Hall PTR. 1999
[5] H. Korth, & A. Silberschatz,., ''Database System Concepts''. 5th ed., McGraw-Hill, ISBN 007-124476-x. 2006
[6] R. Elmasri. B. Navathe., ''Fundamental of Database Systems'', Fifth Edition, Published by Pearson Education, ISBN 978-81-317-1625-0. 2009
[7] A. J. Singh, & R. Chauhan, ''E-Government Databases: A Retrospective Study'', Indian Journal of Computer Science and Engineering, Vol 1 No 2, 66-73, ISSN: 0976-5166. 2011
[8] L. Davidson, ''Ten Common Database Design Mistakes'', http://www.simple-talk.com/sql/database-administration/ten-common-database-design-mistakes.On-line, Accessed on : 22 July, 2010
[9] M. Blaha, ''Referential Integrity is important for databases'', Modelsoft Consulting Corp, www.odbms.org/experts.aspx. 2008 Accessed on: 15 July 2010.
[10] K. Tangwongson, P. Himabindu, M.Kaminsky, '' Efficient Similarity Estimation for Systems Exploiting Data Redundancy''. In Proc. IEEE INFOCOM, (San Diego, CA), Mar. 2010.
[11] Loser, C. Legner, and D. Gizanis, ''Master Data Management for Collaborative ServiceProcesses'', http://www.alexandria.unisg.ch/EXPORT/DL/Christine_Legner/28091.pdf, Accessed on 28 Jan., 2010.

Balvir Singh, A.J. Singh, Rajesh Chauhan, "Analysis of Database of University Examination System: A Case Study", International Journal of Computer Sciences and Engineering, Vol.04, Issue.05, pp.43-49, 2016.