Accurate Estimation of software service Development Effort is a great challenge both in industry and for academia. The concept of Effort is an important and effective parameter in process Development and software service management. The reliable Estimation of Effort helps the project managers to allocate the resources better and manage cost and time so that the project will be finished in the determined time and budget. One of the most popular Effort Estimation methods is analogy base Estimation (ABE) to compare a service with similar historical cases. Unfortunately ABE is not capable of generating accurate results unless determining weights for service features. Therefore, this paper aims to make an efficient and reliable model through combining ABE method and DE algorithm to estimate the software services Development Effort. In fact, the DE algorithm was utilized for weighting features in the similarity function of the ABE method. The proposed hybrid model has been evaluated on a real data set and two artificial datasets. The obtained results were compared with common Effort Estimation methods. Obtained values indicate 28, 34 and 19 percentage improvement on the three datasets ISBSG, Moderate, and Severe, respectively.

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.

One of important aspects of software project management is required cost and time Estimation for developing the information system.One of the important concerns for a project manager is Estimation of required Effort for Development of an information system.Therefore many Effort Estimation models have been proposed. Learning- based methods, such as neural networks, is one of them.The aim of this study is use of RBF neural networks to estimate the necessary Effort which is required for developing an information system.The result of this study shows that, this network in comparing with the model-based methods gives a suitable Estimation about required Effort for Development of the system.

Accuracy in estimating the needed Effort for software Development caused software Effort Estimation to be a challenging issue. Beside Estimation of total Effort, determining the Effort elapsed in each software Development step is very important because any mistakes in enterprise resource planning can lead to project failure. In this paper, a Bayesian belief network was proposed based on effective components and software Development process. In this model, the feedback loops are considered between Development steps provided that the return rates are different for each project. Different return rates help us determine the percentages of the elapsed Effort in each software Development step, distinctively. Moreover, the error measurement resulted from optimized Effort Estimation and the optimal coefficients to modify the model are sought. The results of the comparison between the proposed model and other models showed that the model has the capability to highly accurately estimate the total Effort (with the marginal error of about 0.114) and to estimate the Effort elapsed in each software Development step.

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).

Accurate Estimation of required Effort for software Development has an important role in success of such projects. So far, a lot of research work has been conducted to estimate the Effort, but improving the precision of this calculation is still a challenge. In this paper, an approach is proposed based on the metaheuristic algorithms to solve this challenge. The procedure is as follows. First, the Cuckoo Search algorithm is used in order to select the correct software features in estimating Effort. Then, the results are further analyzed by Particle Swarm Optimization algorithm. The idea is that the sequential application of these algorithms has led to more accurate search of the problem space and possibility of achieving the global optimum, i. e. the best features is increased. Finally, the selected features are used as the input parameters of the COCOMO II post-architecture model and the Effort is estimated. The proposed approach is evaluated on two datasets of COCOMO 81 and COCOMO NASA and in order to its evaluation, two metrics, namely the median magnitude of relative error and the percentage of prediction are used. The results obtained from the experiments of this approach and their comparison to the results of the previous works show that on the COCOMO 81, the value of the median magnitude of relative error decreased by 0. 177 and the percentage of prediction, for the three values of 25, 30 and 40 percent, increased by 7. 87%, 8. 04% and 8. 66%, respectively. Furthermore, on the COCOMO NASA, the value of the median magnitude of relative error decreased by 0. 151 and the percentage of prediction, for the three values of 25, 30 and 40 percent, increased by 7. 55%, 7. 98% and 8. 11%, respectively.

Nowadays the Effort Estimation of software Development is crucial in Software projects management. Not only have the accurate estimate of cost help customers and investors, but also it will be effective in rational decision-making in the implementation and management of software projects. Various Estimation models have been invented and used so far. Many of the current Effort Estimation approaches are adopted by collecting data from previous projects. Case-based reasoning (CBR) is one of the successful techniques of Effort Estimation in software projects. This method alone is not very accurate, a defect which can be corrected by creating hybrid models. In this paper, CBR was combined with two separate metaheuristic algorithms including particle swarm optimization (PSO) and the firefly algorithm to propose a new hybrid model. Then the performance of the proposed model was evaluated. According to the results of the proposed model on Cocomo, Albrecht and Maxwell datasets, the firefly algorithm showed an acceptable performance.

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.