Enhanced Software Effort Estimation Using Multi Layered Feed Forward Artificial Neural Network Technique (original) (raw)
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A New High Performance Neural Network Model for Software Effort Estimation
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In this research, it is concerned with concerned with constructing software effort estimation models based on artificial neural network. The model is designed accordingly to improve the performance of the network that suits to the COCOMO model. Recent year the software industry is growing rapidly and people pay more attention on how to keep high efficiency in the process of software development and management. In the process of software development , time, cost, manpower are all critical factor. At the stage of software project planning, project manager will evaluate these parameter to get an efficient software develop process. Software effort evaluate is an important aspect which includes amount of cost, schedule, and manpower requirement. In this paper, it is proposed to use multilayer feed forward neural network to accommodate the model and its parameter to estimate software development effort. The network is trained with back propagation learning algorithm by iteratively process...
Software Effort Estimation with Different Artificial Neural Network
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Failures of software are mainly due to the faulty project management practices, which includes effort estimation. Continuous changing scenarios of software development technology makes effort estimation more challenging. Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a potential tool for estimation. This paper presents a performance analysis of different ANNs in effort estimation. We have simulated four types of ANN created by MATLAB10 NNTool using NASA dataset.
A Efficient Neural Network Model for Software Effort Estimation
Software development effort estimation is the process of predicting the effort required to develop a software system. Estimating development effort accurately in the early stage of software life cycle plays a crucial role in effective project management. Effort estimation is a key factor for software project success, defined as delivering software of agreed quality and functionality within schedule and budget. Traditionally effort estimation has been used for planning and tracking project resources. It has become an important task. This paper proposed a neural network model for software effort estimation. This model has 3 layers. The train, validation and test data used are from COCOMO data set. Inputs and targets data randomly divided in train (60 %), validation (20%) and test (20%) group. When the number of neurons in hidden layer was 20, Number of training samples was 37, number of validation samples was 13 and number of testing samples was 13, the network has best performance. In this case, the value of training, validation and testing MSE was 0.01044, 0.0475 and 0.0375 respectively and value of training, validation and testing R was 0.9167, 0.7741 and 0.7410 respectively.
Predicting Software Development Effort Using Artificial Neural Network
International Journal of Software Engineering and Knowledge Engineering, 2010
Software effort estimation is an important and integral part of software development life cycle of any project. However, cost, time and manpower estimation is required prior to implementation of the project. The objective of this work is to explore the possibilities of application of Artificial Neural Network (ANN) as a tool for predicting software development effort. We proposed an ANN model for predicting software development effort. A multilayer feed forward network is trained using back-propogation algorithm and demonstrated to be suitable. This study used the training and validation data, which is randomly selected from the data repository of 650 projects [8]. The experimental results indicate that the Mean Absolute Relative Error (MARE) is 0.261 of ANN model and shows that ANN model is a competitive model for predicting software development effort.
Neural Network Models for Software Development Effort Estimation: A Comparative Study
Software development effort estimation (SDEE) is one of the main tasks in software project management. It is crucial for a project manager to efficiently predict the effort or cost of a software project in a bidding process, since overestimation will lead to bidding loss and underestimation will cause the company to lose money. Several SDEE models exist; machine learning models, especially neural network models, are among the most prominent in the field. In this study, four different neural network models – Multilayer Perceptron, General Regression Neural Network, Radial Basis Function Neural Network, and Cascade Correlation Neural Network – are compared with each other based on: (1) predictive accuracy centered on the Mean Absolute Error criterion, (2) whether such a model tends to overestimate or underestimate, and (3) how each model classifies the importance of its inputs. Industrial datasets from the International Software Benchmarking Standards Group (ISBSG) are used to train and validate the four models. The main ISBSG dataset was filtered and then divided into five datasets based on the productivity value of each project. Results show that the four models tend to overestimate in 80% of the datasets, and the significance of the model inputs varies based on the selected model. Furthermore, the Cascade Correlation Neural Network outperforms the other three models in the majority of the datasets constructed on the Mean Absolute Residual criterion.
Neural Network based Software Effort Estimation: A Survey
Software effort estimation is used to estimate how many resources and how many hours are required to develop a software project. The accurate and reliable prediction is the key to success of a project. There are numerous mechanisms in software effort estimation but accurate prediction is still a challenge for the researchers and software project managers. In this paper, the use of Neural Network techniques for Software Effort Estimation is discussed and evaluate on the basis of MMRE and Predicate. At the end, a specific Neural Network based Effort Estimation technique is proposed for further research.
2012
Software effo rt estimation guides the bedding, planning, development and maintenance process of software product. Software development uses different paradigm like: procedure oriented, object oriented, Agile, Incremental, component based and web based etc. Different co mpanies use different techniques for their software project development. The available estimation techniques are not suitable for all types of software develop ment techniques. So there is a need of estimation technique that can be applied on all type of software. This paper we are evaluating the application of artificial neural networks in prediction of effort in conventional and Object Oriented Soft ware development approach. We have used feed-forward neural netwo rk created using MATLAB10 ( NN tool kit ) and applied on two different types of datasets, one for conventional software and another for object oriented software. The simulat ion results were studied and we found that artificial neural network model works very accurately on both types of software development techniques.
Artificial Neural Networks Based Analysis of Software Cost Estimation Models
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.
Software Effort Estimation Using Adaptive Fuzzy-Neural Approach
International Journal of Computer Applications Technology and Research, 2017
Software effort estimation is an important step in software development. It predicts the amount of effort and development time required to build a software system. It is one of the most important tasks and an accurate estimate is vital to the successful completion of the project. Building software effort estimation requires developing sound computational models. This paper investigates the use of fuzzy-neural systems in estimating software effort. A comparison is made with a radial basis neural network. Results obtained based on the China dataset indicates that a hybrid model that combine fuzzy inferencing with neural networks ability to learn from examples provided more accurate results than using neural networks alone.
Automation of Software Cost Estimation using Neural Network Technique
International Journal of Computer Applications, 2014
Software cost estimation is one of the most challenging tasks in software engineering. Over the past years the estimators have used parametric cost estimation models to establish software cost, however the challenges to accurate cost estimation keep evolving with the advancing technology. A detailed review of various cost estimation methods developed so far is presented in this paper. Planned effort and actual effort has been comparison in detail through applying on NASA projects. This paper uses Back-Propagation neural networks for software cost estimation. A model based on Neural Network has been proposed that takes KLOC of the project as input, uses COCOMO model parameters and gives effort as output. Artificial Neural Network represents a complex set of relationship between the effort and the cost drivers and is a potential tool for estimation. The proposed model automates the software cost estimation task and helps project manager to provide fast and realistic estimate for the project effort and development time that in turn gives software cost.