A Survey on Software Cost Estimation Techniques <s> Analogy Based <s> The aim of this study is to provide an efficient methodology in estimating project development cost using analogy. Cost estimation is one of the greatest challenges in software industry to be successful enough in delivering a project within the schedule and with quality. In most cases, the delivered product either loses out on the quality or the expected timeline, owing to improper and imprecise estimation of the project cost. Deriving near accurate project cost could be done using analogy, wherein previous project data set is manipulated to arrive at the accurate cost for the current project. Ant Colony Optimization (ACO) technique implemented over analogy provides a better solution to overcome the challenges faced during cost estimation. In our study, we follow a three step methodology, based on ACO to arrive at the project development cost from promised datasets. Firstly, we extract the matching projects from data set based on the nearest values of a parameter. Secondly, we identify and group projects based on sizing. Thirdly, we improve similarity measures to match our project against those in data set. <s> BIB001 </s> A Survey on Software Cost Estimation Techniques <s> Analogy Based <s> Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: 1 classical analogy-based technique and 2 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy. <s> BIB002
The analogy was used in the past as a method of supporting or supporting explanations of a particular natural phenomenon which was later incorporated in the software development sector of software development of for the purposes of software cost estimations BIB002 . Analogy Based is one of the most efficient approaches in software cost estimation due to its outstanding performance and capability of handling complex datasets  . The model exploits comparison as the main form of a subject to compare software project under considerations with past historical projects which have prior known characteristics, schedule and efforts. Thus, this model has to rely on previous projects similar to the ones that are to be developed thus the levels of accuracy are usually very high  . Conventional analogy-based models usually deploy the same number of analogies in all the projects that it is involved with similar data sets which improves on the estimation approximations. There are some researchers that claim using the same number of analogies could only improve on the data sets only and not the entire project. Thus, when exploiting this model, it is important to understand the characteristics of each data sets so as to be able to discover the optimum set of analogies for each project BIB001 . Accuracy levels can only be increased when users of the model are aware of the different data characteristics instilled by each data set. Through the comparison of various datasets, the model is used to approximate the time that a project would take for it to be completed. In cost estimation, the model is relied on to give empirical evidence on the probability of certain software features being accurate. It is also flexible and intuitive in nature as it can be applied in a variety of circumstances where other algorithmic modeling and estimating techniques don't operate BIB002 . As much as it is said to be dependent upon past history so as to execute its functions, it can still operate even where past data is not available. However, there are still some setbacks while using the model as it lacks appropriate analogs BIB001 . For instance, despite it being widely used in the industries it has not stipulated standards on how it should be exploited for expert opinion-based opinion. The common advantage that most users of the method are familiar with is the ability of the model to avoid bootstrapping of cost BIB001 . On the extreme end, it is accompanied by a major disadvantage which is its inability to operate with categorical data. Hence more implementations need to be done on the model so as to enable to operate with all types of data (Table 1 ).