Neuro fuzzy—COCOMO II model for software cost estimation

ACM Sigsoft Software Engineering Notes, 2010

Effective Software cost estimation is one of the most challenging and important activities in Software development. The software industry does not estimate projects well. In this paper we have represented size in KLOC as a Fuzzy number. A new model is presented using fuzzy logic to estimate effort required in software development. We use MATLAB for tuning the parameters of famous COCOMO model. The performance of model is evaluated on published software projects data. Comparison of results from our model with existing prevalent models is done.

2015

Software cost estimation is the process of predicting the effort required to develop a software system. Many estimation models have been proposed over the last 30 years. With the development of software engineering, estimation of project cost and duration has been a very important work. It plays an important role in project bid and project planning. Many papers have been published regarding this topic, which aims at predicting costs of projects to a tolerable degree of accuracy at the early stage. In this paper, several existing fuzzy logic methods for software cost estimation are illustrated and they are compared with the intermediate COCOMO model. Comparing the features of the methods, it could be applied for clustering based on abilities and is also useful for selecting the special method for each project.

International Journal of Advanced Computer Science and Applications, 2013

Budgeting, bidding and planning of software project effort, time and cost are essential elements of any software development process. Massive size and complexity of now a day produced software systems cause a substantial risk for the development process. Inadequate and inefficient information about the size and complexity results in an ambiguous estimates that cause many losses. Project managers cannot adequately provide good estimate for both the effort and time needed. Thus, no clear release day to the market can be defined. This paper presents two new models for software effort estimation using fuzzy logic. One model is developed based on the famous COnstructive COst Model (COCOMO) and utilizes the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model utilize the Inputs, Outputs, Files, and User Inquiries to estimate the Function Point (FP). The proposed fuzzy models show better estimation capabilities compared to other reported models in the literature and better assist the project manager in computing the software required development effort. The validation results are carried out using Albrecht data set.

ACM Sigapp Applied Computing Review, 2000

Estimation of effort/cost required for development of software products is inherently associated with uncertainty. In this paper, we are concerned with a fuzzy set-based generalization of the COCOMO model (f-COCOMO). The inputs of the standard COCOMO model include an estimation of project size and an evaluation of other parameters. Rather than using a single number, the software size can be regarded as a fuzzy set (fuzzy number) yielding the cost estimate also in form of a fuzzy set. The paper includes detailed results with this regard by relating fuzzy sets of project size with the fuzzy set of effort. The analysis is carried out for several commonly encountered classes of membership functions (such as triangular and parabolic fuzzy sets). The issue of designer-friendliness of the f-COCOMO model is discussed in detail. Here we emphasize a way of propagation of uncertainty and ensuing visualization of the resulting effort (cost). Furthermore we augment the model by admitting software systems to belong partially to the three main categories (namely embedded, semidetached and organic) and discuss key implications of this generalization and highlight its links with a generalized sensitivity analysis. The experimental part of the study illustrates the approach and contrasts it with the standard numeric version of the COCOMO model.

One of the most important tasks of a software development life cycle is to estimate software time and effort. Accuracy in effort estimation is still a great challenge in the software industry. Conventional models like the Constructive Cost Model (COCOMO) are quite helpful but they are unable to manage the unsure nature of the software projects particularly within the early part of the development criterion. There has been considerable work exhausted in this area using neural networks and conjointly some work done using fuzzy logic. These models offer additional exactitude however they " re heavily obsessed on the scale of the training set. The aim of this paper is to develop and evaluate a neuro-fuzzy model to estimate software development effort. Our proposed work is compared with neural network approach and fuzzy logic approach based on the value of MMRE (Mean of Magnitude of Relative Error) .The projected Neuro-fuzzy model was found to have lower MMRE value.

Software Engineering especially project planning, scheduling, monitoring and control are based on accurate estimate of the cost and effort. In the initial stage of Software Development Life Cycle (SDLC), it is hard to accurately measure software effort that may lead to possibility of project failure. Here, an empirical comparison of existing software cost estimation models based on the techniques used in those models has been elaborated using statistical criteria. On the basis of findings of empirical evaluation of existing models, a Neuro-Fuzzy Software Cost Estimation model has been proposed to hold best practices found in other models and to optimize software cost estimation. Proposed model gives good result as compared to other considered software cost estimation methods for the defined parameters in overall but it is also dependent on type of project, data and technique used in implementation.

Managers required administering software development projects with accurate estimates of the resources. So accurate estimates can increase the speed of the effort for developing software projects, and prevent probabilistic failure consequently. Therefore, in the process of software development estimate must be taken in order to reduce cost and time schedule and the existence of probabilistic risks. The implementation of software projects developmental process in large organizations with hundreds of experts and professionals often is a complicated action and needs to be estimated. Estimation cost, time and manpower depends to factors such as the performance of software groups and the complexity of software project. Different models have been proposed for the Software Cost Estimation (SCE) that COCOMO model is the most common model for SCE. This model was used in 1981 by Boehm. This model is a high risk and threat for software projects due to its low accuracy and lack of reliability. Therefore, there is a need to evaluate and estimate SCE using Artificial Intelligence (AI) models. One of the best models of AI for SCE is Artificial Neural Networks (ANNs). ANNs try to assess and evaluate the data with minimal error using instruction and learning techniques. In this paper, the performance of Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Wavelet Neural Network (WNN), Functional Link Artificial Neural Network (FLANN) and Generalized Regression Neural Network (GRNN) models have been discussed in SCE.

Applied Soft Computing, 2007

Accurate software development cost estimation is important for effective project management such as budgeting, project planning and control. So far, no model has proved to be successful at effectively and consistently predicting software development cost. A novel neuro-fuzzy Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of a neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, the proposed model can be interpreted and validated by experts, and has good generalization capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. In addition, it allows input to have continuous rating values and linguistic values, thus avoiding the problem of similar projects having large different estimated costs. A detailed learning algorithm is also presented in this work. The validation using industry project data shows that the model greatly improves estimation accuracy in comparison with the well-known COCOMO model. #

Estimating the work-effort and the schedule required to develop and/or maintain a software system is one of the most critical activities in managing software projects. Software cost estimation is a challenging and onerous task. Estimation by analogy is one of the convenient techniques in software effort estimation field. However, the methodology used for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and accurate manner. Different techniques have, so far, been used like regression analysis, mathematical derivations, simulation, neural network, genetic algorithm, soft computing, fuzzy logic modelling etc. This paper aims to utilize soft computing techniques to improve the accuracy of software effort estimation. In this approach fuzzy logic is used with particle swarm optimization to estimate software development effort. The model has been calibrated on 30 projects, taken from NASA dataset. The results of this model are compared with COCOMO II and Alaa Sheta Model. The proposed model yields better results in terms of MMRE.

2020

Software cost prediction is the technique of accurately evaluating the amount while developing the software. Estimation involves the total time required for the completion of the software, effort required that is measured in terms of person per month (PM), and the total cost to complete the activity. Accuracy and duration are the two desirable criteria in the software estimation process. In software estimation process, there are several inputs that are being fed to the system and these inputs are used for the generation or calculation of the set of outputs. The important work of the software project managers in the present scenario is the computation of cost or effort before the absolute advancement of any particular software. There are several methods applied for software cost estimation but we will focus on the fuzzy logic which is a soft-computing method. We feel that model which is based on fuzzy logic for the software cost estimation should be able to give the uncertain values ...