Fuzzy Logic Control Neural Network Hybrid System for Identification, Classification of Software Reusability Components through Relationships of Lattice Factors

  • Abstract
  • Keywords
  • References
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  • Abstract

    The software reusability mode is highly required field for successful execution of artificial intelligence, machine learning based applications to fulfill the present and future human needs. The identification, classification and measuring the required components are key-roles concerns in fast development of software reusability components for producing the high quality software. This paper is proposing the Fuzzy Logic Controller Neural Network Hybrid System which is implicated to recognize the affecting factors of component reusability execution by instituting the strong, week relationships in among these considered factors to fulfill the user requirement. This approach considered eleven effecting factors such as Portability, Reliability, Complexity, Efficiency, Quality, Security, Cost, Maintainability, Cohesion, Availability and Flexibility along with their related attribute metrics. This paper has composed with four major objectives such as: the comparative analysis of Fuzzy Logic Control System and Neural Networks with their advantages and execution flow; The implications of Fuzzy Logic Control Neural Network Hybrid System architecture design for concern problem; The proposed FLCNNHS based algorithm and execution data flow diagram for executing the considered software reusability effecting factors along with their supporting attributes metrics for identification and Classification of Reusability Components through Strong, Week Relationships of Lattice Factors which is implacable for designing the better quality software product; and described the experimental analysis and results through proposed algorithmic approach. This innovative approach is more helpful for software developers to choose highly accurate components which are more required to build the high efficiency secure systems.


  • Keywords

    Fuzzy Logic Control System (FLCS), Fuzzy Logic Control Neural Network Hybrid System (FLCNNHS), Neural Network (NN), Prior-Knowledge (PK).

  • References

      [1] Arun Sharma, P. S. Grover, Rajesh Bhatia, Reusability Assessment for Software Components – a Neural Network Based Approach, ACM SIGSOFT Software Engineering Notes 34(2):1-6, DOI: 10.1145/1507195.1507215, February 2009

      [2] Shrddha Sagar, N. W. Nerurkar, Arun Sharma, “A soft computing based approach to estimate reusability of software components”, ACM SIGSOFT Software Engineering, ACM New York, NY, USA, doi:10.1145/1811226.1811235, Volume 35 Issue 4, Pages 1-4, July 2010.

      [3] Kirti Tyagi, Arun Sharma, “An adaptive neuro fuzzy model for estimating the reliability of component-based software systems”, Applied Computing and Informatics, Production and hosting by Elsevier, pp:38–51, (2014) 10,

      [4] Parvinder S. Sandhu and Hardeep Singh, “Automatic Reusability Appraisal of Software Components using Neuro-fuzzy Approach”, World Academy of Science, Engineering and Technology International Journal of Computer and Systems Engineering, Vol:1, No:8, 2007.

      [5] W. Humphrey, Managing the Software Process, SEI Series in Software Engineering, Addison-Wesley, 1989.

      [6] L. Sommerville, Software Engineering, Addision-Wesley, 4th Edition, 1992.

      [7] R. S. Pressman, Software Engineering: A Practitioner’s Approach, McGraw-Hill Publications, 5th edition, 2005.

      [8] G. Boetticher and D. Eichmann, “A Neural Network Paradigm for Characterising Reusable Software,” Proceedings of the 1st Australian Conference on Software Metrics, 18-19 November 1993.

      [9] S. V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic-Basic Concepts and Applications, IEEE Press, 1996, pp. 153-160.

      [10] Shrddha Sagar, Pratistha Mathur and Arun Sharma, “Software Quality Estimation of Component Based Software System by Using Fuzzy MOORA Approach”, International Journal of Artificial Intelligence and Application for Smart Devices, ISSN: 2288-6710 IJAIASDVol.5, No.1 (2017), pp.21-30, 2017.

      [11] Shalini Goel, Arun Sharma, Neuro Fuzzy based Approach to Predict Component’s Reusability, International Journal of Computer Applications (0975 – 8887) Volume 106 – No.5, November 2014

      [12] SOFTWARE: PRACTICE AND EXPERIENCE Softw. Pract. Exper. 2017; 47:941–942, Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/spe.2504, Published online 12 May 2017.

      [13] J-S. R. Jang and C.T. Sun, “Neuro-fuzzy Modeling and Control,” Proceeding of the IEEE, March 1995.

      [14] Santosh Kumar Henge, Dr B.Rama, “Neural Fuzzy Closed Loop Hybrid System for Classification, Identification of Mixed Connective Consonants and Symbols with Layered Methodology” ((ICPEICES-2016)”, Pages: 2880-2887 and published in IEEE Xplore: DOI: 10.1109/ICPEICES.2016.7853708, INSPEC accession number: 16672805, ISBN: Electronic ISBN: 978-1-4673-8587-9, Print on Demand (PoD) ISBN: 978-1-4673-8588-6, added date: 16 Feb 2017.

      [15] Nagib C. Callaos B. (1994) ―Designing with Systemic Total Quality‖ Educational Technology Vol. 34, Issue.1, Page(s) 29-36.

      [16] Grady R B. (1992) ―Practical Software Metrics for Project Management and Process improvement Publication: Book , Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 1992.

      [17] Tomar P. and Gill N. ― New Algorithm for Component Selection to Develop Component-Based Software with X Model‖ Lecture Notes on Software Engineering COTS Component Selection‖ Journal of Object Technology, Vol. 4, 2005.

      [18] Alvaro A. de Almeida E.S. ; Meira S.L. ―A Software Component Quality Model: A Preliminary Evaluation‖ Published in: Software Engineering and Advanced Applications. SEAA '06. 32nd EUROMICRO Conference, Page(s): 28-37, Cavtat, Dubrovnik, 2006.

      [19] Kumar, Vijai, Arun Sharma, Rajesh Kumar, and P. S. Grover, Quality aspects for component‐based systems: A metrics based approach Software: Practice and Experience, pp. 1531-1548, 2012.

      [20] McCall,J. A., Richards P K. and Walters G F. ―Factors in Software Quality‖ Springfield VA National Technical Information Service, Vol. 3, Issue. 5, Page(s): 133-139, 1977.

      [21] Sharma, A., Kumar, R., Grover, P. S., Empirical Evaluation and Validation of Interface Complexity Metrics for Student Components, International Journal of Software Engineering and Knowledge Engineering, Vol. 18, Issue 7, pp. 919—931, 2008.

      [22] Boehm B W. ―Characteristics of Software Quality TRW Series of software Technology‖ Amsterdam North Holland, 1978

      [23] M.Bertoa and A. Vallecillo ―Quality Attributes for COTS Components‖ proceedings of the 6th International ECOOP Worshop on Quantitative Approaches in Object-Oriented Software Engineering (QAOOSE), Malaga, 2002.

      [24] Pande J. ―On Some Critical ssues in Component Selection in Component based Software Development‖ nternational Journal of Computer Applications, Vol. 46, pp. 0975-8887, 2012.

      [25] Pande.J., Bisht.R.K., Pant.D. Pathak.V.K. ―On Some Quality ssues of Component Selection in CBSD‖ J. Software Engineering & Applications Vol. 3 pp. 556-560, 2010.

      [26] Dromey R.G. (1995) ―A Model for Software Product Quality‖ Published in: EEE Transactions on Software Engineering, Vol. 21 , Issue. 2, Page(s): 146 – 162.

      [27] Ince D. ― ISO 9001 and Software Quality Assurance‖ McGraw-Hill, New York, 1994.

      [28] Bertoa M F. and Vallecillo A. ―Usability Metrics for Software Components‖ QAOOSE’04:Proceedings of the 8th ECOOP Workshop on Quantitative Approaches in Object-Oriented Software Engineering. Page(s): 1- 10, 2004.

      [29] Kumar R. Sharma A. and Grover P.S. ―Predicting Maintainability of Component-based Systems by using Fuzzy-Logic‖ Communications in Computer and information Science Springer Berlin Heidelberg, USA, Vol. 40, Issue 11, pp. 581-593, 2009.

      [30] Lloyd, Wesley James, "A Common Criteria Based Approach for COTS Component Selection", Journal of Object Technology, V0l. 4, pp. 25-32, 2005.

      [31] Santosh Kumar Henge, Dr B.Rama, “Five Layered-Neural Fuzzy Closed Loop Hybrid Control System with Compound Bayesian Decision Making Process for Classification cum Identification of Mixed Connective Conjunct Consonants and Numerals”, Published in “© Springer Nature Singapore Pte Ltd”. 2017, Advances in Computer and Computational Sciences, “Advances in Intelligent Systems and Computing-553”, pp.619-629, DOI: 10.1007/978-981-10-3770-2_58, 2017.

      [32] Tyagi k. Sharma A. ―Significant Factors for Reliability Estimation of Component Based Software Systems‖ Journal of Software Engineering and Applications, Vol. 7, pp.934-942, 2014.

      [33] Madaan N. and Kaur J. ― A Survey on Selection Techniques of Component Based Software‖ International Journal of Informatics and Computation technology, Vol. 4, pp. 1245-1250, 2014.

      [34] Santosh Kumar Henge, Dr B.Rama, “Comparative Analysis of Soft Computing Hybrid Intellectual System Implications for Salvation of Machine Learning based Complex Problems”, in International Journal of Advanced Scientific Technologies, Engineering and Management Sciences (IJASTEMS-ISSN: 2454-356X) Volume.3,Special Issue.1, Page No: 15-27, March.2017.




Article ID: 16845
DOI: 10.14419/ijet.v7i3.8.16845

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