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The estimation of software development effort is a critical task for the effective management of any software industry. Despite the fact that it has been under development for a long time - along with many contributions from many authors seeking to improve the accuracy of software effort estimation, it is still of great interest to many researchers. This study proposed an improved effort estimation model, named the Productivity Optimising Model. This model was designed, based on the Function Points Measurement method and the Multiple Linear Regression model. The Multiple Linear Regression model was built based on the research of historical datasets in order to provide an estimation model so that one can determine the optimising productivity, and then it is easy to calculate the effort. The effort result of this model was compared to the others that were calculated by the Mean Value of Productivity of the tested dataset, and the Capers Jones method. It proved that proposed method gives better accuracy results than the other models. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Context: Software cost and effort estimation are important activities for planning and estimation of software projects. One major player for cost and effort estimation is functional size of software which can be measured in variety of methods. Having several methods for measuring one entity, converting outputs of these methods becomes important. Objectives: In this study we investigate different techniques that have been proposed for conversion between different Functional Size Measurement (FSM) techniques. We addressed conceptual similarities and differences between methods, empirical approaches proposed for conversion, evaluation of the proposed approaches and improvement opportunities that are available for current approaches. Finally, we proposed a new conversion model based on accumulated data. Methods: We conducted a systematic literature review for investigating the similarities and differences between FSM methods and proposed approaches for conversion. We also identified some improvement opportunities for the current conversion approaches. Sources for articles were IEEE Xplore, Engineering Village, Science Direct, ISI, and Scopus. We also performed snowball sampling to decrease chance of missing any relevant papers. We also evaluated the existing models for conversion after merging the data from publicly available datasets. By bringing suggestions for improvement, we developed a new model and then validated it. Results: Conceptual similarities and differences between methods are presented along with all methods and models that exist for conversion between different FSM methods. We also came with three major contributions for existing empirical methods; for one existing method (piecewise linear regression) we used a systematic and rigorous way of finding discontinuity point. We also evaluated several existing models to test their reliability based on a merged dataset, and finally we accumulated all data from literature in order to find the nature of relation, Javad Amiri: Nabshe Kooche 3, Bolvare shadi, Farhangian 2, Qom, Iran, phone: +989127476593 Vinod Kumar: s/o P.V.Kondala Rao, Main Road Khaji Street Rajahmundry. A.P. India pin: 533101 phone: +917396449336