A Survey on Software Cost Estimation Techniques <s> Background and Related Work <s> This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertisedbased techniques, learningdoriented techniques, dynamicsdbased models, regressiondbased models, and compositedBayesian techniques for integrating expertisedbased and regressiondbased models. Experience to date indicates that neuraldnet and dynamicsdbased techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates. <s> BIB001 </s> A Survey on Software Cost Estimation Techniques <s> Background and Related Work <s> Estimation of desired effort is one of the most important activities in software project management. This paper presents an approach for estimation based upon machine learning techniques for non-quantitative data and is carried out in two phases. The first phase concentrates on selection of optimal feature set in high dimensional data, related to projects undertaken in past. A quantitative analysis using Rough Set Theory is performed for feature reduction. The second phase estimates the effort based on the optimal feature set obtained from first phase. The estimation is carried out differently by applying Naive Bayes Classifier and Artificial Neural Network techniques respectively. The feature reduction process in first phase considers public domain data (USP05). The performance of the proposed methods is evaluated and compared based on the parameters such as Mean Magnitude of Relative Error (MMRE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient. It is observed that Naive Bayes classifier achieved better results for estimation when compared with that by using Neural Network technique. <s> BIB002
Several studies and systematic reviews related to the software cost estimation to techniques have been conducted to date  - BIB002 . The SLR papers gave the timeline of the cost estimation methods while the studies conducted gave a discussion on the existing cost estimation methods. There is still a research gap in listing out the popular cost estimation techniques and discussing the strength and weakness of those cost estimation techniques which aids the researchers and practitioners to choose which estimation technique to opt for depending on their needs. According to Jianfeng et al., software development effort estimation (SDEE) is a process that is used by the project managers or the software developers in predicting the effort required to develop a software system  . Over the Figure 2 . Software cost estimation techniques  . Journal of Software Engineering and Applications years since the 1990's, researchers have suggested the implementation of Machine Learning (ML) models and SDEE in improving on estimation accuracy. Although there have been numerous researches on the process still there lacks empirical evidence of comparisons of different software cost estimation techniques. There are ongoing studies and researches that are underway to give detailed literature on the issue of software cost estimations as depicted by Sharma et al.  . The current researchers are aimed at improving o the functionality of the different cost estimation approaches that are used in software cost estimation. According to Jorgensen et al., it is of great relevance for the users of these approaches to be well conversant with each approach so as to aid in selecting the appropriate technique to deploy in software cost estimation  . Current studies show that in order for us to be able to understand the achievements that have been met since the software cost estimation process began it would be of importance to review the methods back to decades when they were exploited BIB001 . Since then to the current year, companies have been exploiting different taxonomies and classification criteria in identifying the suitable method to support their estimations  . The first journals and reports were published between 50's and 70's which proves that studies on software cost estimation existed in the past despite them being conducted manually  . With numerous studies being perfumed on the process we are aware that the entire process entails software plans, resources, coding, testing, development and design. Numerous organizations are dependent upon software development for their stability and sustainability in relation to cost estimations. According to  , it is important for the cost of software to be estimated in such a way that it will not compromise quality, efficiency and timeline. The former methods that were exploited in the sector were dependent on source line of code (SLOC), cost drivers and function points BIB001 . However current studies prove that there is a lot of automation in the software cost estimation process with each technique being implemented to fit the desired functions  . There are basic reasons as to why there is a tremendous research going on in software cost estimation process; proper budgeting, accurate estimations, software improvement investment analysis, project planning and control, trade-off and risk analysis.