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.

Managing Software projects due to its intangible nature is full of challenges when predicting the Effort needed for development. Accordingly, there exist many studies with the attempt to devise models to estimate Efforts necessary in developing Software. According to the literature, the accuracy of estimator models or methods can be improved by correct application of data filtering or feature weighting techniques. Numerous models have also been proposed based on machine learning methods for data modeling. This study proposes a new model consisted of data filtering and feature weighting techniques to improve the Estimation accuracy in the final step of data modeling. The model proposed in this study consists of three layers. Tools and techniques in the first and second layers of the proposed model select the most effective features and weight features with the help of LSA (Lightning Search Algorithm). By combining LSA and an artificial neural network in the third layer of the model, an estimator model is developed from the first and second layers, significantly improving the final Estimation accuracy. The upper layers of this model filter out and analyze data of lower layers. This arrangement significantly increased the accuracy of final Estimation. Three datasets of real projects were used to evaluate the accuracy of proposed model, and the results were compared with those obtained from different methods. The results were compared based on performance criteria, indicating that the proposed model effectively improved the Estimation accuracy.

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.

In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of Software project Effort Estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data, and also the complexity of the models used for Effort Estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of Software project Effort Estimation. This technique has been tested on well-known datasets. Implementation results indicate that the resulting subset, compared to the original dataset, has produced better outcomes in terms of Effort Estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving Effort Estimation accuracy.

The Software Effort Estimation plays an important role in Software project management, and analogy-based Estimation (ABE) is the most common method used for this purpose. ABE estimates the Effort required for a new Software project based on its similarity to the previous projects. A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between the projects and the Effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in the analogy-based Software project Effort Estimation. The proposed approach is implemented and tested on two well-known datasets of Software projects. The performance of the proposed model is compared with the other optimization algorithms based on the MMRE, MDMRE, and PRED (0. 25) measures. The results obtained showed that the weighted ABE models provide more accurate and better Effort estimates relative to the unweighted ABE models and that the hybrid PSO-SA approach leads to better and more accurate results compared to the other weighting approaches in both datasets.

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.

Project management in Software development is one of the most crucial activities as it encompasses the entire Software development process from start to finish. Estimating the Effort required for Software projects is a significant challenge in project management. Managing Software projects and consequently estimating their Effort for more efficient and impactful management of such projects is necessary and unavoidable. Analogy-based Estimation in Software Effort Estimation involves comparing new projects to completed ones. However, this method can be ineffective due to variations in feature importance and dependencies. To address this, weights are assigned to features using optimization techniques like meta-heuristic algorithms. Yet, these algorithms may get stuck in local optima, yielding nonoptimal results. An approach to estimate Software Effort is proposed in this study. It aims to find global optimal feature weights by combining particle swarm and genetics metaheuristic algorithms. This hybrid approach leverages particle motion and composition to enhance solution generation, increasing the likelihood of finding the global optimum and overcoming local optima issues. The algorithm calculates feature weights for project Estimation using analogy-based methods. The proposed approach was tested and assessed using two datasets, namely Maxwell and Desharnais. The experimental results indicated an enhancement in the evaluation criteria, including MMRE, MdMRE, and PRED, compared to similar research works.

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.