A Survey on Software Cost Estimation Techniques <s> Fuzzy Logic <s> The process of estimating time and cost required for developing software is called software cost estimation. It is one of the steps to be carried out in project planning. Early software estimation models are based on regression analysis or mathematical derivations. Today’s models are based on simulation, neural network, genetic algorithm, soft computing, fuzzy logic modeling etc. This paper aims to utilise an adaptive fuzzy logic model to improve the accuracy of software time and cost estimation. Using advantages of fuzzy set and fuzzy logic can produce accurate software attributes which result in precise software estimates. 63 Historic projects of NASA dataset having COCOMO format is used in the evaluation of the proposed Fuzzy Logic COCOMO II. Eight membership functions available in fuzzy logic are used and a comparison is made to find out which membership function yields better result in terms of Mean Magnitude of Relative Error (MMRE) and PRED (25%). <s> BIB001 </s> A Survey on Software Cost Estimation Techniques <s> Fuzzy Logic <s> Software development has become an important activity for many modern organisations. Software engineers have become more and more concerned about accurately predicting the cost and quality of software product under development. In the last few decades many software cost estimation models have been developed but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper we propose the use of Radial Basis Function Network RBFN for software cost estimation using Intuitionistic Fuzzy C Means IFCM with Gaussian potential functions. This technique selects the most desirable cluster centres, thereby increasing the clustering accuracy which results in improved software cost estimations. A comparison of RBFN using IFCM, Fuzzy C Means FCM and conventional COCOMO model is presented. The datasets used in our study are the COCOMO81 dataset and NASA93 dataset. Experimental results are given to show the effectiveness of the proposed method. <s> BIB002
The model is a computing approach that is based on the degrees of truth rather than the unusual true or false normally referred to as Boolean logic which most of the modern computers are based on BIB002 . Additionally, it can be said to be an Journal of Software Engineering and Applications approach to computing based on many-valued function for instance, instead of a task completed or not, one can say 50% is completed. Fuzzy Logic (FL) approach gives an acceptable but definite output which is usually in response to inaccurate (fuzzy), distorted, incomplete and ambiguous input. FL was developed in 1965 by Loft Zadeh from a concept of fuzzy set theory. The first system is the one that is exploited in the estimation sector as most of the systems produce crisps data as input and expect the same type of data as output. There are three steps that have to be followed while using FL; the first is the Fuzzification which converts a crisp into a fuzzy set BIB002 . The second is the Fuzzy Rule-based System, at the step after all the crisp input has been fuzzified into their respective linguistic values the inference engine then derives their linguistic values BIB001 . Defuzzification is the final step which involves the conversion of fuzzy output into crisp output. Fuzzy logic is deployed for decision making whereby it can be implemented with various sizes and abilities ranging from small microcontrollers to large workstation-station based software development. In the sector of software cost estimation, FL has been used to give acceptable reasoning although it does not guarantee accurate reasoning BIB001 . This approach is very easy to use despite the fact that it has numerous functions that it can execute when it comes to cost estimation of the software development BIB001 . Also, with the approach, it is advantageous as estimators of software are in a position to estimate and give an anticipation of the different aspects of the project even before the initiation level. The method has also a fast learning ability compared to other methods under the same scope BIB002 . The major disadvantage that the approach lacks guarantee of accuracy which is a vital part of the software cost estimation as to avoid incurring financial losses. Through the defuzzification process, the model is able to interact the output into a numerical value as per the desire. There are a number of three applied approaches under this method which include; 2FA-kmodes it is used in clustering where numerical datasets are shown through fuzzy sets however the fuzz k-modes algorithm is used to maintain categorical attributes. The second approach is 2FA-kprototype it is used in clustering also where project data are clustered into homogeneous sets BIB002 . The final approach is the classical analogy it is used to predict the effort of the target project using information from former similar projects. The first two models 2FA-knodes, 2FA-kprototypes perform same and are both better than classical analogy. The similarity in the functionality of the two models is technically for the purposes of increasing efficiency within the model BIB001 . The major disadvantage associated with the two models is that they are not very good in COCOMO dataset and classical analogy doesn't recognize categories as "less risky", "medium", and "high". Thus, there is a need for implementations to be conducted for the model so as to enable future proper operation in case they are deployed in COCOMO and other datasets.