Accurate estimating is one of the most important activities in the field of software project management. Different aspects of software projects must be estimated among which time and Effort are of significant importance to efficient project planning. Due to complexity of software projects and lack of information at the early stages of project, reliable Effort Estimation is a challenging issue. In this paper, a hybrid model is proposed to estimate the Effort of software projects. The proposed model is a combination of particle swarm optimization algorithm and a linear regression method in which coefficient finding is optimally performed.Moreover, the Estimation equation is adjusted using project size metric so that the most accurate estimate is achieved. A relatively real large data set is employed to evaluate the performance of the proposed model and the results are compared with other models. The obtained results showed that the proposed hybrid model can improve the accuracy of estimates.

predicting the Effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the Development of useful models to predict the costs of software product Development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest Effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the Estimation of Effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of Effort Estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neurofuzzy- genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type- 1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models.

One of important aspects of software projects is estimating the cost and time required to develop projects. Nowadays, this issue has become one of the key concerns of project managers. Accurate Estimation of essential Effort to produce and develop software is heavily effective on success or failure of software projects and it is highly regarded as a vital factor. Failure to achieve convincing accuracy and little flexibility of current models in this field have attracted the attention of researchers in the last few years. Despite improvements to estimate Effort, no agreement was obtained to select Estimation model as the best one. One of Effort Estimation methods which is highly regarded is COCOMO. It is an extremely appropriate method to estimate Effort. Although COCOMO was invented many years ago, it enjoys the Effort Estimation capability in software projects. Researchers have always attempted to improve the Effort Estimation capability in COCOMO through improving its structure. However, COCOMO results are not always satisfactory. The present study introduces a hybrid model for increasing the accuracy of COCOMO Estimation. Combining bee colony algorithm with COCOMO Estimation method, the proposed method obtained more efficient coefficient relative to the basic mode of COCOMO. Selecting the best coefficients maximizes the efficiency of the proposed method. The simulation results revealed the superiority of the proposed model based on MMRE and PRED (0.15).

One of the most important aspects of software project management is the Estimation of cost and time required for running information system. Therefore, software managers try to carry Estimation based on behavior, properties, and project restrictions. Software cost Estimation refers to the process of Development requirement prediction of software system. Various kinds of Effort Estimation patterns have been presented in recent years, which are focused on intelligent techniques. This study made use of clustering approach for estimating required Effort in software projects. The Effort Estimation is carried out through SWR (Step Wise Regression) and MLR (Multiple Linear Regressions) regression models as well as CART (Classification And Regression Tree) method. The performance of these methods is experimentally evaluated using real software projects. Moreover, clustering of projects is applied to the Estimation process. As indicated by the results of this study, the combination of clustering method and algorithmic Estimation techniques can improve the accuracy of estimates.

Nowadays, e ort Estimation in software Development is of great value and signi cance in project management. Accurate and appropriate cost Estimation not only helps customers trust to invest but also has a signi cant role in logical decision making during project management. Di erent models of cost Estimation are presented and employed to the date, but the models are application speci c. In this paper, a three-phase hybrid approach is proposed to overcome the problem. In the  rst phase, features are selected using a combination of genetic algorithm and the perceptron neural network. In the second phase, impact factors are associated to each selected feature using multiple linear regression methods which act as coe cients of in uence for each feature. In the last and the third phase, the feature weights are optimized by Imperialist Competitive Algorithm. To compare the proposed model for e ort Estimation with state-of-the-art models, three datasets are chosen as benchmark, namely COCOMO, Maxwell and Albrecht. The datasets are standard and publicly available for assessment. The experiments show promising results and average performance is improved by the proposed model for MMRE performance criterion on the datasets by 23%, 38% and 35%, respectively.