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

Towards an Early Software Estimation Using Log-Linear Regression and a Multilayer Perceptron Model

Software estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effortbased on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects

Using actors and use cases for software size estimation

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based meth-ods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method. © 2021 by the author. Licensee MDPI, Basel, Switzerland.Faculty of Applied Informatics, Tomas Bata University in Zli

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant

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

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

Algorithmic optimisation method for improving use case points estimation

This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance.Department of Computers and Communication System of the Faculty of Applied Informatics (Tomas Bata University in Zlin

An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review

[EN] Software developers require effective effort estimation models to facilitate project planning. Although Usman et al. systematically reviewed and synthesized the effort estimation models and practices for Agile Software Development (ASD) in 2014, new evidence may provide new perspectives for researchers and practitioners. This article presents a systematic literature review that updates the Usman et al. study from 2014 to 2020 by analyzing the data extracted from 73 new papers. This analysis allowed us to identify six agile methods: Scrum, Xtreme Programming and four others, in all of which expert-based estimation methods continue to play an important role. This is particularly the case of Planning Poker, which is very closely related to the most frequently used size metric (story points) and the way in which software requirements are specified in ASD. There is also a remarkable trend toward studying techniques based on the intensive use of data. In this respect, although most of the data originate from single-company datasets, there is a significant increase in the use of cross-company data. With regard to cost factors, we applied the thematic analysis method. The use of team and project factors appears to be more frequent than the consideration of more technical factors, in accordance with agile principles. Finally, although accuracy is still a challenge, we identified that improvements have been made. On the one hand, an increasing number of papers showed acceptable accuracy values, although many continued to report inadequate results. On the other, almost 29% of the papers that reported the accuracy metric used reflected aspects concerning the validation of the models and 18% reported the effect size when comparing models.This work was supported by the Spanish Ministry of Science, Innovation and Universities through the Adapt@Cloud Project under Grant TIN2017-84550-R.Fernández-Diego, M.; Méndez, ER.; González-Ladrón-De-Guevara, F.; Abrahao Gonzales, SM.; Insfran, E. (2020). An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review. IEEE Access. 8:166768-166800. https://doi.org/10.1109/ACCESS.2020.3021664S166768166800