OPTIMIZED MODEL FOR SOFTWARE EFFORT ESTIMATION USING COCOMO-2 METRICS WITH FUZZY LOGIC

: Effort estimation is the crucial activity during the planning phase of any project. The successful delivery of the software project is directly dependent on the accuracy of software effort estimation in planning phase. As effort multiplier have significant influence on the COCOMO-II and this research proposed the model for improving the precision of effort estimation using fuzzy logic on COCOMO-II effort multipliers. Fuzzy Logic is a rule based architecture which runs on binary pattern. It has Input set, associated with rule-sets based on the membership function. There are three membership functions i


INTRODUCTION
The most substantial activity in software project management is Software development effort prediction. Software effort prediction at early stages of project development holds great significance for the industry to meet the competitive demands of today's world. Accuracy, reliability and precision in the estimates of software effort are highly desirable. The globalization result in high competition between software industries. And so, estimation task has become one of the most crucial tasks inside software development course of action. Due to different issues in computer software development global computer software estimates are not exact, which leads to your great loss. Estimation of software effort sometime causes overestimation or underestimation. In both the cases software effort estimation carried out is imprecise, so companies have grown more unwavering in calculating accurate computer software effort estimation. This reflects that software cost estimation is often a complex task. Estimation models may be introduced for dealing with such problems, it is available in three categories [1] [2]:algorithmic model consisting of COCOMO model, perform points etc., non-algorithmic style expert and machine learning. Number of estimation models may be developed but none has appeared perfect. The most popular algorithmic estimation models which include Boehm's COCOMO [3], Putnam's SLIM [4] and Albrecht's Function Point [5]. These models require as inputs, accurate estimate of some attributes such as line of code (LOC), complexity and so on which are difficult to obtain during the early stage of a software project development. The models also have difficulty in modelling the inherent complex relationships between the contributing factors, are unable to handle categorical data as well as lack of reasoning capabilities [6]. By this paper, I have focused on the fuzzy-logic model useful for estimation process, which provides much more accurate and sensitive results as compared with other estimation models. Fuzzy logic focused COCOMO II models are highly made for software effort estimation specially when there are uncertain or imprecise data. Fuzzy logic can be put under machine studying estimation model [7].

THE COCOMO FRAMEWORK
The Constructive Cost Model (COCOMO) is an algorithmic software cost estimation model developed by Barry W. Boehm. The model uses a basic regression formula with parameters that are derived from historical project data and current project characteristics. Boehm proposed 3 modes of projects: 1. Organic mode -simple projects that engage small teams working in known and stable environments. 2. Semi-detached mode -projects that engage teams with a mixture of experience. It is in between organic and embedded modes. 3. Embedded mode -complex projects that are developed under tight constraints with changing requirements. According to the Boehm's, the basic COCOMO equation takes the following form: Where, D is estimated development time in months. The coefficients a b , b b , c b , d b are given in table these coefficients are constraints for different category for software products.

FUZZY LOGIC
Since fuzzy logic foundation by Lotfi Zadeh in 1965, it has been the subject of important investigations [10]. It is a mathematical tool for dealing with uncertainty and also it provides a technique to deal with imprecision and information granularity [11]. The fuzzy logic model uses the fuzzy logic concepts introduced by Lotfi Zadeh [10].The membership! (") of an element x of a classical set A, as subset of the universe X, is defined by (2), as follows: A system based on FL has a direct relationship with fuzzy concepts (such as fuzzy sets, linguistic variables, etc.) and fuzzy logic. The popular fuzzy logic systems can be categorized into three types: pure fuzzy logic systems, Takagi and Sugeno's fuzzy system and fuzzy logic system with fuzzifier and defuzzifier [12]. Since most of the engineering applications produce crisp data as input and expects crisp data as output, the last type is the most widely used one fuzzy logic system with fuzzifier and defuzzifier. It was first proposed by Mamdani. It has been successfully applied to a variety of industrial processes and consumer products [13].

MEMBERSHIP FUNCTIONS
Below three membership functions are used [14]: The parameters a and c locate the "feet" of the triangle and the parameter b locates the peak.

EVALUATION CRITERIA
The evaluation consists in comparing the accuracy of the estimated effort with the actual effort. There are many evaluation criteria for software effort estimation introduced in the literature, among them we will apply the most frequent evaluation criteria [15]

CONCLUSIONS
Many researchers contributed towards the software effort estimation and presented the different model to minimize the gap between estimated and actual effort. But no single model reach to the satisfactory level due to the influence of different factors till the completion of the project. In this paper we presented a COCOMO II fuzzy model of software effort estimation. Due to the Fuzzification of 17 effort multiplier, the model will minimize the imprecision in estimated effort. The implementation of this model includes Fuzzification, fuzzy rule generation and defuzzification. Our future work will present the comparison of estimated effort with actual effort based on this model using the data set that contains records of 14 different years, starting from 1971 and ending at 1987, 93 NASA projects.