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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.
Software Effort Estimation with Different Artificial Neural Network
2011
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.
Experimental Analysis of Effort Estimation Using Artificial Neural Network
2012
Failures of software are mainly due to the faulty project management practices, which incl ude effort estimation. Continuous changing scenarios of softwa re development technology make effort estimation mo re challenging. Accurate software cost estimates are critical to bo th developers and customers. They can be used for g enerating request for proposals, contract negotiations, scheduling, monitoring and control. The exact relationship betwen the attributes of the effort estimation is difficult to establish. A neural network is good at discovering relationships and pattern in the data. So, in this paper we will discuss how we predict th e effort using Neural Network learning techniques a nd a comparative analysis of different ANNs in effort estimation.
A New High Performance Neural Network Model for Software Effort Estimation
2014
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...
Procedia Computer Science, 2016
Software Effort Estimation models are hot topic of study over 3 decades. Several models have been developed in these decades. Providing accurate estimations of software is still very challenging. The major reason for such disappointments in projects are because of inaccurate software development norms; effort estimation is one such practice. Dynamically fluctuating environment of technology in software development industry make effort estimation further perplexing. One of the most commonly used algorithmic model for estimating effort in industry is COCOMO. Capability of machine learning particularly Artificial Neural Networks is to adjust a complex set of bond among the various independent and dependent variables. The paper proposes usage of ANN (Artificial Neural Network) based model technologically advanced using Multi Layered Feed Forward Neural Network which is given training with Back Propagation training method. COCOMO data-set is accustomed to test and train the network. Mean-Square-Error (MSE) and Mean Magnitude of Relative-Error (MMRE) are used as performance measurement indices. The experiment outputs suggest that the suggested model can provide better results and accurately forecast the software development effort.
Effort Estimation with Different Artificial Neural Network
2011
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. KeywordsEffort Estimation, Artificial Neural Network, NNtool, MMRE
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.
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.
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.