Software Metric and Back Propagation Techniques Applied in Fuzzy Logic

Zindhu S., Aswini G., Anusha Prem I.

Abstract


Software quality is one of the most important factors in the software development. It can be depends on many attributes. One of the best techniques in Fuzzy Logic is metrics and maintainability. This paper presents the application of fuzzy logic in software metrics. Software metric is the measurement of the software development process and product. It can be used as variables in the project management. The most common types of these models are predicting the development effort for a software system based on size, complexity, characteristics and metrics. There are many problems that have not been overcome using the traditional techniques of both formal and linear regression model. Once the problem faced by managers, who are using project metrics models is the elicitation of numerical inputs. These problems can be seen as collective failure of software measurement. The proposed techniques can help to overcome some of the difficulties by representing the imprecision in both input and output. This techniques especially fuzzy logic is investigated and some usable recommendation is made. Different levels of available information and desired precision can be used differently, mainly depends on current phase, although a single model can be used for consistency.

 


Keywords


Fuzzy Logic, Neural Network, Backpropgation, Software Metrics

Full Text:

PDF

References


N.E. Fenton and S. L. Pfleeger. Software Metrics: A Rigorous & Practical Approach PWS, 1997.

A. Gray and S. MacDonell. Application of fuzzy logic to software metric models for development effort

estimation.

Y. Miyazaki, M. Terakado, K. Ozaki and N. Nozaki. Robust regression for developing software estimation models. Journals of System and Software.

Grimstad, S., Jorgensen, M., Molokken-Ostvold, K., Software Effort Estimation Terminology. The Tower of

Babel. Information and software Technology. Elsevier, s2005

R.S. Pressman. Software Engineering: A.Practitioner’s Approach. McGraw-Hill, fourth edition, 1997.

S.G. MacDonell and A.R.Gray. Fulsome. A fuzzy logic modeling tool for software metricians.

A.A. Moataz, O.S.Moshood, A.Jarallah, “Adaptive fuzzylogic-based framework for software developmenteffort

prediction”, Information and Software Technology.

JaswinderKaur, Satwinder Singh, Dr. Karanjeet Singh Kahlon, PourushBassi,“Neural Network-A

NovelTechnique for Software Effort Estimation”, International Journal of Computer Theory and Engineering.

Ali Idri and Taghi M. Khoshgoftaar& Alain Abran,”Can Neural Networks be easily Interpreted in Software

CostEstimation”, IEEE Transaction.

Molokken K.; Jorgensen M., 2003. A review of software surveys on software effort estimation, Proceedings of

IEEE International Symposium on Empirical Software Engineering


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.