International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 11, No. 1, 8 Jan. 2019
Cover page and Table of Contents: PDF (size: 639KB)
Effort Estimation of Back-end Part of Software using Chaotically Modified Genetic Algorithm
Full Text (PDF, 639KB), PP.32-42
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Author(s)
Saurabh Bilgaiyan
1,*
Dhiraj Kumar Goswami
1
Samaresh Mishra
1
Madhabananda Das
1
Index Terms
SDEE, genetic algorithm (GA), evolutionary learning (EL), dilation-erosion perceptron (DEP), mathematical morphology (MM)
Abstract
The focus of Software Development Effort Estimation (SDEE) is to precisely predict the estimation of effort and time required for successfully developing a software project. From the past few years, data-intensive applications with a huge back-end part are contributing to the overall effort of projects. Therefore, it is becoming more important to add the back-end part to the SDEE process. This paper proposes an Evolutionary Learning (EL) based hybrid artificial neuron termed as dilation-erosion perceptron (DEP) framework from the mathematical morphology (MM) having its foundation in complete lattice theory (CLT) for solving the SDEE problem. In this work, we used the DEP (CMGA) model utilizing a chaotically modified genetic algorithm (CMGA) for the construction of DEP parameters. The proposed method uses the ER diagram artifacts such as aggregation, specialization, generalization, semantic integrity constraints, etc. for calculating the SDEE of back-end part of the business software. Furthermore, the proposed method was tested over two different datasets, one is existing and the other one is a self-developed dataset. The performance of the given method is then evaluated by three popular performance metrics, exhibiting better performance of the DEP (CMGA) model for solving the SDEE problems.
Cite This Paper
Saurabh Bilgaiyan, Dhiraj Kumar Goswami, Samaresh Mishra, Madhabananda Das, "Effort Estimation of Back-end Part of Software using Chaotically Modified Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.1, pp.32-42, 2019. DOI:10.5815/ijisa.2019.01.04
Reference
[1]M. Padmaja, D. Haritha. “Software Effort Estimation Using Grey Relational Analysis”, International Journal of Information Technology and Computer Science, ISSN: 2074-9015, Vol. 9, Issue: 5, pp. 52-60, May-2017.
[2]S. Goyal, A. Parashar, “Machine Learning Application to Improve COCOMO Model using Neural Networks”, International Journal of Information Technology and Computer Science (IJITCS), Vol. 3, pp. 35-51, March-2018.
[3]S. Bilgaiyan, S. Sagnika, S. Mishra, M N. Das, “A Systematic Review on Software Cost Estimation in Agile Software Development”, Journal of Engineering Science and Technology Review, ISSN: 1791-2377, Vol. 10, Issue: 4, pp. 51-64, September-2017.
[4]P. Pospieszny, B. Czarnacka-Chrobot, A. Kobylinski, “An effective approach for software project effort and duration estimation with machine learning algorithms”, The Journal of Systems and Software, Vol. 137, pp. 184–196, March-2018.
[5]Y. Zao, H B K. Tan, W. Zhang, “Software Cost Estimation through Conceptual Requirement”, In Third International Conference on Quality Software, IEEE, pp. 141-143, November-2003.
[6]G J. Kennedy, “Elementary Structures in Entity-Relationship Diagram: A New Metric of Effort Estimation”, In International Conference on Software Engineering: Education and Practice. IEEE, pp. 86-92, January-1996.
[7]S. Mishra, P. Pattnaik, R. Mall, “Early Estimation of Back-End Software Development Effort”, International Journal of Computer Applications, ISSN: 0975–8887, Vol. 33, Issue: 2, pp. 6-11, November-2011.
[8]S. Mishra, R. Mall, “Estimation of Effort Based on Back-End Size of Business Software Using ER Model”, In 2011 World Congress on Information and Communication Technologies, IEEE, ISSN: 2074-9015, pp. 1098-1103, December-2011.
[9]B. Jamil, J. Ferzund, A. Batool, S. Ghafoor, “Empirical Validation of Relational Database Metrics for Effort Estimation”, In 6th International Conference on Networked Computing, IEEE, ISSN: 2074-9015, pp. 1-5, May-2010.
[10]S. Parida, S. Mishra, “Review report on Estimating the Back-End Cost of Business Software Using ER- Diagram Artifact”, International Journal of Computer Science and Engineering Technology (IJCSET), ISSN: 2229-3345, Vol. 2, Issue: 1, pp. 233-238, March-2014.
[11]S. Mishra, E. Aisuryalaxmi, R. Mall, “Estimating Database Size and its Development Effort at Conceptual Design Stage”, In Global Trends in Information Systems and Software Applications. Springer, Vol. 270, Issue: 1, pp. 120-127, 2012.
[12]S. Mishra, K C. Tripathy, M K. Mishra, “Effort Estimation Based on Complexity and Size of Relational Database System”, International Journal of Computer Science and Communication, ISSN: 2074-9015, Vol. 1, Issue: 2, pp. 419-422, July-December-2010.
[13]S. Bilgaiyan, K. Aditya, S. Mishra, M. Das, “A Swarm Intelligence based Chaotic Morphological Approach for Software Development Cost estimation”, International Journal of Intelligent Systems and Applications, ISSN: 2074-9058, Vol. 10, Issue: 9, pp. 13-22, September-2018.
[14]R de A. Araujo, A L I. Oliveira, S. Soaresand, S. Meira, “An Evolutionary Morphological Approach for Software Development Cost Estimation” Neural Networks. Elsevier, Vol. 32, Issue: 1, pp. 285-291, August-2012.
[15]P. Sussner, E L. Esmi, “Morphological Perceptrons with Competitive Learning: Lattice-Theoretical Framework and Constructive Learning Algorithm”, Information Sciences, Elsevier, Vol. 181, Issue: 10, pp. 1929-1950, May-2011.
[16]R de A. Araujo, S. Soares, A L I. Oliveira, “Hybrid Morphological Methodology for Software Development
Cost Estimation”, Expert Systems with Applications, Elsevier, Vol. 39, Issue: 6, pp. 6129-6139, May-2012.
[17]A L I. Oliveira, P L. Braga , R M F. Lima, M L. Cornélio, “GA-based Method for Features Selection and Parameters Optimization for Machine Learning Regression applied to Software Effort Estimation”, Information and Software Technology, Elsevier, Vol. 51, Issue: 11, pp. 6129-6139, November-2010.
[18]G J F. Banon, J. Barrera, “Decomposition of Mappings between Complete Lattices by Mathematical Morphology, part 1. General lattices”, Signal Processing, Elsevier, Vol. 30, Issue: 3, pp. 299–327, February-1993.
[19]S. Bilgaiyan, K. Aditya, S. Mishra, M N. Das, “Chaos-based Modified Morphological Genetic Algorithm for Software Development Cost Estimation”, Progress in Computing, Analytics and Networking, Vol. 710, pp. 31-40, April-2018.
[20]F. Leung , H. Lam, S. Ling, “Tuning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm”, IEEE Transactions on Neural Networks, ISSN: 1045-9227, Vol. 14, Issue: 1, pp. 79-88, February-2003.
[21]S. Bilgaiyan, S. Sagnika, S. Mishra, M N. Das, “Study of Task Scheduling in Cloud Computing Environment Using Soft Computing Algorithms”, International Journal of Modern Education and Computer Science, ISSN: 2075-017X, Vol. 7, Issue: 3, pp. 32-38, March-2015.
[22]W F. Gao, S Y. Liu, L L. Huang, “Particle Swarm Optimization with Chaotic Opposition-Based Population Initialization and Stochastic Search Technique”, Communications in Nonlinear Science and Numerical Simulations. Elsevier, Vol. 17, Issue: 11, pp. 4316-4327, November-2012.
[23]A. Hussain, Y. S. Muhammad, M. N. Sajid, “An Efficient Genetic Algorithm for Numerical Function optimization with Two New Crossover Operators”, International Journal of Mathematical Sciences and Computing, ISSN: 2310-9025, Vol. 4, Issue. 4, pp. 41-55, November-2018.
[24]A. Raha, M. K. Naskar, et al. “A Genetic Algorithm Inspired Load Balancing Protocol for Congestion Control in Wireless Sensor Networks using Trust Based Routing Framework (GACCTR)”, International Journal of Computer Network and Information Security, ISSN: 2074-9090, Vol. 9, Issue. 9, pp. 9-20, July-2013.
[25]R de A. Araujo, A L I. Oliveira, S. Soares, “A Shift-Invariant Morphological System for Software Development Cost Estimation” Expert Systems with Applications, Elsevier, ISSN: 2074-9015, Vol. 38, Issue: 4, pp. 4162-4168, April-2011.
[26]M P. Clements, D F. Hendry, “On the Limitations of Comparing Mean Square Forecast Errors” Journal of Forecasting, ISSN: 2074-9015, Vol. 12, Issue: 8, pp. 617-637, December-1993.