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).

The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order to increase the accuracy of this process, estimation models have to be revised. The Constructive Cost Model (COCOMO) has often been referred as an efficient model for software cost estimation. The popularity of COCOMO is due to its flexibility; it can be used in different environments and it covers a variety of factors. In this paper, we aim to improve the accuracy of cost estimation process by enhancing COCOMO model. To this end, we analyze the cost drivers using meta-heuristic algorithms. In this method, the improvement of COCOMO is distinctly done by effective selection of coefficients and reconstruction of COCOMO. Three metaheuristic optimization algorithms are applied synthetically to enhance the process of COCOMO model. Eventually, results of the proposed method are compared to COCOMO itself and other existing models. This comparison explicitly reveals the superiority of the proposed method.

Nowadays, software cost estimation (SCE) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. In this paper, we present a new hybrid model of multi-layer perceptron (MLP) artificial neural network (ANN) and ant colony optimization (ACO) algorithm for high accuracy in SCE called Multilayer Perceptron Ant Colony Optimization (MLPACO). Current research uses some of features for increasing accuracy of estimation among of the existing parameters has been considered for effort estimation in software projects, and then these selected features will be filtered by ACO algorithm in order to reach highest accuracy in estimation and optimization of MLP ANN method. The results show that this novel approach with high accuracy for more than 80% cases is better than algorithmic constructive cost model (COCOMO) in the majority cases. Also, the results of proposed algorithm show that mean magnitude of relative error (MMRE) in the proposed algorithm is lower than COCOMO model.

We can say a software project is successful when it is delivered on time, within the budget and maintaining the required quality. However, nowadays software cost estimation is a critical issue for the advance software industry. As the modern software’ s behaves dynamically so estimation of the effort and cost is significantly difficult. Since last 30 years, more than 20 models are already developed to estimate the effort and cost for the betterment of software industry. Nevertheless, these algorithms cannot satisfy the modern software industry due to the dynamic behavior of the software for all kind of environments. On this study, an empirical interpolation model is developed to estimate the effort of the software projects. This model compares with the COCOMO based equations and predicts its result analyzing individually taking different cost factors. The equation consists one independent variable (KLOC) and two constants a, b which are chosen empirically taking different NASA projects historical data and the results viewed in this model are compared with COCOMO model with different values of scale factor. In this paper the author analyze more than 250 projects collected from PROMISE repository. The effort variance is very low and the proposed model has the lowest Mean Magnitude of Relative Error (MMRE) and RMSSE.

Nowadays, data mining is one of the most significant issues. One field of data mining is a mixture of computer science and statistics which is considerably limited due to increase in digital data and growth of computational power of computers. One of the domains of data mining is the software cost estimation category. In this article, classifying techniques of learning algorithm of machine and COCOMO model as the most common estimation model of software costs are presented. Then, the analysis method of principal component approach is presented. This article presents a method to improve the performance of software cost estimation is suitable. Moreover, the basic data set is decreased and is turned into a new collection by using this method. Among the features, the best are extracted. The algorithms of several classifications are assessed by applying this method. Finally, the evidence for accuracy of our claims in terms of increase in estimation accuracy of software costs is presented.

The software productions are very expensive projects with uncertainty. For these reasons, the estimation of time and cost of software Is very important for both producers and consumers. In this paper an efficient three- stage algorithm is developed for software production cost and time estimation. In the first stage, the required person- month for implementation of software production are obtained by COCOMO and function point methods. We integrated these two methods to consider all aspects in software production and increase estimation accuracy. In the second stage the required duration for completion of each step of production (planning, analysis, design and so on) is obtained by paired comparisons matrix. In third stage, tables of complete time and cost of software are concluded by GERT network in project control and work break structure (WBS). In whole of all stages of this paper, triangular fuzzy numbers are used to express uncertainty existed in succession and repetition of each production step, time of beginning, ending, the duration of each task and costs of them. Retrieved results examined by 30 practical projects and conclude accuracy of 93 percent for time estimation and 92 percent for cost one. Also suggested algorithm is more accurate than COCOMOІІ 2000 algorithm as 50 percent based on examined problems.