Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation

محورهای موضوعی : Embedded Systems

Zahra Barati ¹ , Mahdi Jafari Shahbazzadeh ² , Vahid Khatibi Bardsiri ³

1 - Computer Engineering Department, Kerman Branch, Islamic Azad University, Kerman, Iran.
2 - Electrical Engineering Department, Kerman Branch, Islamic Azad University,Kerman, Iran.
3 - Computer Engineering Department, Bardsir Branch, Islamic Azad University, Kerman, Iran

تاریخ دریافت : 1395/02/09 تاریخ پذیرش : 1395/08/26 تاریخ انتشار : 1395/09/11

کلید واژه: Type-2 fuzzy logic, Fuzzy Logic, Gradient descent, Neuro-Fuzzy, software effort estimation,

چکیده مقاله :

predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of effort estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neuro-fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type-1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models.

منابع و مأخذ:

[1] Mohanty, S.K., Bisoi, A.K., “Software effort estimation approaches-a review”, International Journal of Internet Computing, 2012, 1(3): 82-88.
[2] Gharehchopogh, F. S. and Z. A. Dizaji (2014). A New Approach in Software Cost Estimation with Hybrid of Bee Colony and Chaos Optimizations Algorithms, MAGNT RESEARCH REPORT.
[3] Bardsiri, A.K. and S.M. Hashemi, “Software Effort Estimation: A Survey of Well-known Approaches”. International Journal of Computer Science Engineering (IJCSE), 2014. 3(1): p. 46-50.
[4] Benala, T. R., et al. (2014). Software Effort Estimation Using Data Mining Techniques. ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol I, Springer
[5] Heidrich, J., M. Oivo, and A. Jedlitschka, “Software productivity and effort estimation”. Journal of Software: Evolution and Process, 2015. 27(7): p. 465-466.
[6] Elish, M. O., et al. (2013). "Empirical study of homogeneous and heterogeneous ensemble models for software development effort estimation." Mathematical Problems in Engineering 2013.
[7] Abbas, S.A., et al., “Cost estimation: A survey of well-known historic cost estimation techniques”. Journal of Emerging Trends in Computing and Information Sciences, 2012. 3(4): p. 612-636.
[8] Kumari, S. and S. Pushkar, “Performance analysis of the software cost estimation methods: a review”. International Journal of Advanced Research in Computer Science and Software Engineering, 2013. 3(7): p. 229-238.
[9] Esplanada, P.S. and E.A. Albacea, “Assessing Accuracy of Formal Estimation Models and Development of an Effort Estimation Model for Industry Use”. 2012.
[10] Ramesh, K. and P. Karunanidhi, “Literature Survey On Algorithmic And Non-Algorithmic Models For Software Development Effort Estimation”. International Journal Of Engineering And Computer Science ISSN, 2013: p. 2319-7242.
[11] Potdar, S. M., et al. (2014). "Literature Survey on Algorithmic Methods for Software Development Cost Estimation." International Journal of Computer Technology and Applications 5(1): 183.
[12] Basha, S. and D. Ponnurangam, “Analysis of empirical software effort estimation models”. arXiv preprint arXiv:1004.1239, 2010.
[13] Mendel, J. M. “Type-2 fuzzy sets and systems: an overview” Computational Intelligence Magazine, IEEE , 2007, 2(1): 20-29.
[14] Kashyap, S.K. IR and color image fusion using interval type 2 fuzzy logic system. in Cognitive Computing and Information Processing (CCIP), 2015 International Conference on. 2015. IEE.
[15] Gupta, N. “Comparative study of type-1 and type-2 fuzzy ystems”. Int. J. Eng. Res. Gen. Sci, 2, 2014, 195-198.‏
[16] Hassani, H., & Zarei, J. “Interval Type-2 fuzzy logic controller design for the speed control of DC motors”. Systems Science & Control Engineering, 2015, 3(1), 266-273.‏
[17] Singh, R., Kainthola, A., & Singh, T. N. “Estimation of elastic constant of rocks using an ANFIS approach”. Applied Soft Computing, 2012, 12(1), 40-45.
[18] Malathi, S., & Sridhar, S. “Efficient estimation of effortusing machine-learning technique for software cost”. Indian Journal of Science and Technology, 2012. 5(8), 3194-3196.‏
[19] Lopez-Martin, Cuauhtemoc, “A fuzzy logic model for predicting the development effort of short scale programs based upon two independent variables Applied Soft Computing” , 2011, 724–732.