Fuzzy and Swarm Intelligence for Software Cost Estimation

Hari Ch.V.M.K

Fuzzy and Swarm Intelligence for Software Cost Estimation

tsr.etl * §

Contact

Hari Ch.V.M.K ‡

Contact

§ GITAM Institute of Technology

‡ GITAM University

Fuzzy and Swarm Intelligence for Software Cost Estimation

Article Fingerprint

ReserarchID

CST1BH40

Fuzzy and Swarm Intelligence for Software Cost Estimation Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant

This converted HTML version may contain rendering inconsistencies. Please refer to the PDF for the authoritative version, or click here to provide feedback.

Abstract

Software cost estimation is the process of predicting the amount of time, effort and resources required to complete the project successfully. Software development is a collection of activities includes feasibility study, analysis, design, coding, testing, implementation and maintenance. Each phase requires resources-people, time, software and hardware which should be predicted well before the software development. The prediction means lot of uncertainty. So far many models are proposed by using Fuzzy Logic, Neural Networks, Machine Learning, Regression analysis and Soft Computing techniques. In this paper we are proposed a new model structure basing on Alaa F. Sheta using Fuzzy logic for controlling prediction uncertainty and the parameters of the cost model tuned by using swarm intelligence-Particle Swarm Optimization. The proposed model results are verified with NASA software dataset and results are compared with the existing models. The Results show that the value of MARE (Mean Absolute Relative Error) applying fuzzy-swarm intelligence was substantially lower than MARE of other models exists in the literature.

References

14 Cites in Article

Reference Format

1. John Bailey,Victor Basili (1981). A meta model for software development resource expenditures. proceedings of the Fifth International Conference on Software Engineering, 107-129.
2. C Unknown Title.
3. Chris Kemerer (1987). An empirical validation of software cost estimation models. Communications of the ACM, 30(5), 416-429.
4. . Krishnamurthy,Douglas Fisha (1995). Machine Learning Approaches to estimating software development effort. IEEE Transactions On Software Engineering, 21(2), 126-137.
5. Barry Boehm,Chris Abts,Sunita Chulani (1998). Software Development Cost Estimation Approaches -A Survey. Software Development Cost Estimation Approaches -A Survey, 1, 1-46.
6. Ying Hao (1998). The Takagi-Sugeno fuzzy controllers using the simplified linear control rules are nonlinear variable gain controllers. ELSEVIER: Automatica, 34(2), 157-167.
7. Magne Jørgensen (2005). Evidence-Based Guidelines for Assessment of Software Development Cost Uncertainty. IEEE Transactions On Software Engineering, 31(11), 942-954.
8. Xishi Huang,Danny Ho,Jing Ren,F Luiz,Capretz (2005). Improving the COCOMO model using a neuro-fuzzy approach. Applied Soft Computing, 7, 29-40.
9. Alaa Sheta (2006). Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects. Journal of Computer Science, 2(2), 118-123.
10. Alaa Sheta (2006). Software Effort Estimation and Stock Market Prediction Using Takagi-Sugeno Fuzzy Models. 2006 IEEE International Conference on Fuzzy Systems, 171-178.
11. Alaa Sheta,David Rine,Aladdin Ayesh (2008). Development of software effort and schedule estimation models using Soft Computing Techniques. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 1283-1289.
12. Prasad Reddy,P Sudha,K Rama Sree,Ramesh (2010). Fuzzy Based Approach for Predicting Software Development Effort. International Journal of Software Engineering (IJSE), 1(1), 1-11.
13. Prasad Reddy,P (2010). Particle Swarm Optimization In The Fine Tuning Of Fuzzy Software Cost Estimation Models. International Journal of Software Engineering (IJSE), 1(2), 12-23.
14. Harish Mittal,Pradeep Bhatia (2007). Optimization Criteria for Effort Estimation using Fuzzy Technique. CLEI Electronic Journal, 10(1), 1-11.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

tsr.etl, Hari Ch.V.M.K. . "Fuzzy and Swarm Intelligence for Software Cost Estimation". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 22).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 11 Issue 22

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

COCOMO Fuzzy logic Particle Swarm Optimization Software Cost Estimation Swarm Intelligence

Classification

GJCST Classification D.2.9

Submission ReceivedOctober 30, 2011
Peer Review Double Blind
Handling Editor
Accepted November 24, 2011
Published January 12, 2012

Version of record

v1.2

Issue date

January 12, 2012

Language

English

Order Article Reprint

Experience in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 143

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Srinivasa Rao.T, Prasad Reddy P.V.G.D, Hari Ch.V.M.K (PhD/Dr. count: 0)

View Count (all-time): 329

Total Views (Real + Logic): 6741

Total Downloads (simulated): 407

Publish Date: 2011 12, Fri

Monthly Totals (Real + Logic):

Month 1: 29 views
Month 2: 46 views
Month 3: 66 views
Month 4: 61 views
Month 5: 30 views
Month 6: 53 views
Month 7: 24 views
Month 8: 56 views
Month 9: 39 views
Month 10: 31 views
Month 11: 35 views
Month 12: 38 views
Month 13: 27 views
Month 14: 35 views
Month 15: 29 views
Month 16: 48 views
Month 17: 38 views
Month 18: 38 views
Month 19: 45 views
Month 20: 33 views
Month 21: 40 views
Month 22: 15 views
Month 23: 34 views
Month 24: 32 views
Month 25: 17 views
Month 26: 38 views
Month 27: 37 views
Month 28: 44 views
Month 29: 31 views
Month 30: 36 views
Month 31: 28 views
Month 32: 19 views
Month 33: 13 views
Month 34: 38 views
Month 35: 29 views
Month 36: 39 views
Month 37: 30 views
Month 38: 41 views
Month 39: 39 views
Month 40: 31 views
Month 41: 19 views
Month 42: 42 views
Month 43: 32 views
Month 44: 36 views
Month 45: 45 views
Month 46: 36 views
Month 47: 20 views
Month 48: 43 views
Month 49: 22 views
Month 50: 37 views
Month 51: 16 views
Month 52: 21 views
Month 53: 17 views
Month 54: 26 views
Month 55: 21 views
Month 56: 18 views
Month 57: 40 views
Month 58: 20 views
Month 59: 41 views
Month 60: 34 views
Month 61: 21 views
Month 62: 26 views
Month 63: 13 views
Month 64: 46 views
Month 65: 19 views
Month 66: 14 views
Month 67: 26 views
Month 68: 18 views
Month 69: 28 views
Month 70: 36 views
Month 71: 42 views
Month 72: 22 views
Month 73: 33 views
Month 74: 44 views
Month 75: 28 views
Month 76: 36 views
Month 77: 33 views
Month 78: 35 views
Month 79: 22 views
Month 80: 28 views
Month 81: 14 views
Month 82: 18 views
Month 83: 25 views
Month 84: 19 views
Month 85: 32 views
Month 86: 30 views
Month 87: 33 views
Month 88: 21 views
Month 89: 36 views
Month 90: 30 views
Month 91: 37 views
Month 92: 19 views
Month 93: 38 views
Month 94: 35 views
Month 95: 22 views
Month 96: 31 views
Month 97: 36 views
Month 98: 26 views
Month 99: 26 views
Month 100: 18 views
Month 101: 42 views
Month 102: 25 views
Month 103: 27 views
Month 104: 47 views
Month 105: 40 views
Month 106: 26 views
Month 107: 29 views
Month 108: 32 views
Month 109: 17 views
Month 110: 25 views
Month 111: 24 views
Month 112: 43 views
Month 113: 34 views
Month 114: 34 views
Month 115: 31 views
Month 116: 42 views
Month 117: 26 views
Month 118: 34 views
Month 119: 26 views
Month 120: 20 views
Month 121: 29 views
Month 122: 23 views
Month 123: 17 views
Month 124: 19 views
Month 125: 37 views
Month 126: 27 views
Month 127: 41 views
Month 128: 33 views
Month 129: 22 views
Month 130: 31 views
Month 131: 24 views
Month 132: 37 views
Month 133: 27 views
Month 134: 19 views
Month 135: 36 views
Month 136: 37 views
Month 137: 40 views
Month 138: 36 views
Month 139: 20 views
Month 140: 12 views
Month 141: 32 views
Month 142: 34 views
Month 143: 33 views
Month 144: 35 views
Month 145: 17 views
Month 146: 44 views
Month 147: 15 views
Month 148: 33 views
Month 149: 15 views
Month 150: 24 views
Month 151: 41 views
Month 152: 36 views
Month 153: 28 views
Month 154: 16 views
Month 155: 40 views
Month 156: 24 views
Month 157: 21 views
Month 158: 36 views
Month 159: 17 views
Month 160: 39 views
Month 161: 42 views
Month 162: 26 views
Month 163: 44 views
Month 164: 42 views
Month 165: 40 views
Month 166: 29 views
Month 167: 36 views
Month 168: 40 views
Month 169: 32 views
Month 170: 30 views
Month 171: 25 views
Month 172: 33 views
Month 173: 24 views
Month 174: 17 views
Month 175: 53 views
Month 176: 57 views
Month 177: 49 views
Month 178: 45 views
Month 179: 57 views

Total Views: 6.7K

Total Downloads: 407

All Trends