41 research outputs found
A Treeboost Model for Software Effort Estimation Based on Use Case Points
Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Treeboost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Treeboost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Treeboost model can be used with promising results to estimate software effort
Software Size and Effort Estimation from Use Case Diagrams Using Regression and Soft Computing Models
In this research, we propose a novel model to predict software size and effort from use case diagrams. The main advantage of our model is that it can be used in the early stages of the software life cycle, and that can help project managers efficiently conduct cost estimation early, thus avoiding project overestimation and late delivery among other benefits. Software size, productivity, complexity and requirements stability are the inputs of the model. The model is composed of six independent sub-models which include non-linear regression, linear regression with a logarithmic transformation, Radial Basis Function Neural Network (RBFNN), Multilayer Perceptron Neural Network (MLP), General Regression Neural Network (GRNN) and a Treeboost model. Several experiments were conducted to train and test the model based on the size of the training and testing data points. The neural network models were evaluated against regression models as well as two other models that conduct software estimation from use case diagrams. Results show that our model outperforms other relevant models based on five evaluation criteria. While the performance of each of the six sub-models varies based on the size of the project dataset used for evaluation, it was concluded that the non-linear regression model outperforms the linear regression model. As well, the GRNN model exceeds other neural network models. Furthermore, experiments demonstrated that the Treeboost model can be efficiently used to predict software effort
Comparative Analyisis of Software Cost Estimation Project using Algorithmic Method
Software Cost Estimation has become an important factor to determine the efficiency of software development. There are many model of cost estimation like algorithmic model, top-down, and expert judgement. From all those models, Development in Algorithmic model is higher than the others. In this paper we present a comparative analysis of software cost project using algorithmic methods
Use Case Point Approach Based Software Effort Estimation using Various Support Vector Regression Kernel Methods
The job of software effort estimation is a critical one in the early stages
of the software development life cycle when the details of requirements are
usually not clearly identified. Various optimization techniques help in
improving the accuracy of effort estimation. The Support Vector Regression
(SVR) is one of several different soft-computing techniques that help in
getting optimal estimated values. The idea of SVR is based upon the computation
of a linear regression function in a high dimensional feature space where the
input data are mapped via a nonlinear function. Further, the SVR kernel methods
can be applied in transforming the input data and then based on these
transformations, an optimal boundary between the possible outputs can be
obtained. The main objective of the research work carried out in this paper is
to estimate the software effort using use case point approach. The use case
point approach relies on the use case diagram to estimate the size and effort
of software projects. Then, an attempt has been made to optimize the results
obtained from use case point analysis using various SVR kernel methods to
achieve better prediction accuracy.Comment: 13 pages, 6 figures, 11 Tables, International Journal of Information
Processing (IJIP
Software Effort Estimation Accuracy Prediction of Machine Learning Techniques: A Systematic Performance Evaluation
Software effort estimation accuracy is a key factor in effective planning,
controlling and to deliver a successful software project within budget and
schedule. The overestimation and underestimation both are the key challenges
for future software development, henceforth there is a continuous need for
accuracy in software effort estimation (SEE). The researchers and practitioners
are striving to identify which machine learning estimation technique gives more
accurate results based on evaluation measures, datasets and the other relevant
attributes. The authors of related research are generally not aware of
previously published results of machine learning effort estimation techniques.
The main aim of this study is to assist the researchers to know which machine
learning technique yields the promising effort estimation accuracy prediction
in the software development. In this paper, the performance of the machine
learning ensemble technique is investigated with the solo technique based on
two most commonly used accuracy evaluation metrics. We used the systematic
literature review methodology proposed by Kitchenham and Charters. This
includes searching for the most relevant papers, applying quality assessment
criteria, extracting data and drawing results. We have evaluated a
state-of-the-art accuracy performance of 28 selected studies (14 ensemble, 14
solo) using Mean Magnitude of Relative Error (MMRE) and PRED (25) as a set of
reliable accuracy metrics for performance evaluation of accuracy among two
techniques to report the research questions stated in this study. We found that
machine learning techniques are the most frequently implemented in the
construction of ensemble effort estimation (EEE) techniques. The results of
this study revealed that the EEE techniques usually yield a promising
estimation accuracy than the solo techniques.Comment: Pages: 27 Figures: 15 Tables:
Effort Estimation For Object-oriented System Using Stochastic Gradient Boosting Technique
The success of software development depends on the proper prediction of the effort required to develop the software. Project managers oblige a solid methodology for software effort prediction. It is particularly paramount throughout the early stages of the software development life cycle. Faultless software effort estimation is a major concern in software commercial enterprises. Stochastic Gradient Boosting (SGB) is a machine learning techniques that helps in getting improved estimated values. SGB is used for improving the accuracy of estimation models using decision trees. In this paper, the basic aim is the effort prediction required to develop various software projects using both the class point and the use case point approach. Then, optimization of the effort parameters is achieved using the SGB technique to obtain better prediction accuracy. Furthermore, performance comparisons of the models obtained using the SGB technique with the other machine learning techniques are presented in order to highlight the performance achieved by each method