This study investigates the significance of use case points (UCP) variables and the influence of the complexity of multiple linear regression models on software size estimation and accuracy.Stepwise multiple linear regression models and residual analysis were used to analyse the impact of model complexity. The impact of each variable was studied using correlation analysis.The estimated size of software depends mainly on the values of the weights of unadjusted UCP, which represent a number of use cases. Moreover, all other variables (unadjusted actors' weights, technical complexity factors, and environmental complexity factors) from the UCP method also have an impact on software size and therefore cannot be omitted from the regression model. The best performing model (Model D) contains an intercept, linear terms, and squared terms. The results of several evaluation measures show that this model's estimation ability is better than that of the other models tested. Model D also performs better when compared to the UCP model, whose Sum of Squared Error was 268,620 points on Dataset 1 and 87,055 on Dataset 2. Model D achieved a greater than 90% reduction in the Sum of Squared Errors compared to the Use Case Points method on Dataset 1 and a greater than 91% reduction on Dataset 2. The medians of the Sum of Squared Errors for both methods are significantly different at the 95% confidence level (p < 0.01), while the medians for Model D (312 and 37.26) are lower than Use Case Points (3134 and 3712) on Datasets 1 and 2, respectively

When the Use Case Points method is used for software effort estimation, users are faced with low model accuracy which impacts on its practical application. This study investigates the significance of using subset selection methods for the prediction accuracy of Multiple Linear Regression models, obtained by the stepwise approach. K-Means, Spectral Clustering, the Gaussian Mixture Model and Moving Window are evaluated as appropriate subset selection techniques. The methods were evaluated according to several evaluation criteria and then statistically tested. Evaluation was performing on two independent datasets - which differ in project types and size. Both were cut by the hold-out method. If clustering were used, the training sets were clustered into 3 classes; and, for each of class, an independent regression model was created. These were later used for the prediction of testing sets. If Moving Window was used, then window of sizes 5, 10 and 15 were tested. The results show that clustering techniques decrease prediction errors significantly when compared to Use Case Points or moving windows methods. Spectral Clustering was selected as the best-performing solution, because it achieves a Sum of Squared Errors reduction of 32 % for the first dataset, and 98 % for the second dataset. The Mean Absolute Percentage Error is less than 1% for the second dataset for spectral clustering; 9 % for moving window; and 27 % for Use Case Points. When the first dataset is used, then prediction errors are significantly higher – 53 % for spectral clustering, but Use Case Points produces a 165 % result. It can be concluded that this study proves subset selection techniques as a significant method for improving the prediction ability of linear regression models - which are used for software development effort prediction. It can also be concluded that the clustering method performs better than the moving window method.

Context: Effort estimation is one of the essential phases that must be accurately predicted in the early stage of software project development. Currently, solving problems that affect the estimation accuracy of Use Case Points-based methods is still a challenge to be addressed. Objective: This paper proposes a parametric software effort estimation model based on Optimizing Correction Factors and Multiple Regression Models to minimize the estimation error and the influence of unsystematic noise, which has not been considered in previous studies. The proposed method takes advantage of the Least Squared Regression models and Multiple Linear Regression models on the Use Case Points-based elements. Method: We have conducted experimental research to evaluate the estimation accuracy of the proposed method and compare it with three previous related methods, i.e., 1) the baseline estimation method – Use Case Points, 2) Optimizing Correction Factors, and 3) Algorithmic Optimization Method. Experiments were performed on datasets (Dataset D1, Dataset D2, and Dataset D3). The estimation accuracy of the methods was analysed by applying various unbiased evaluation criteria and statistical tests. Results: The results proved that the proposed method outperformed the other methods in improving estimation accuracy. Statistically, the results proved to be significantly superior to the three compared methods based on all tested datasets. Conclusion: Based on our obtained results, the proposed method has a high estimation capability and is considered a helpful method for project managers during the estimation phase. The correction factors are considered in the estimation process.

Introduction: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group’s Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. Aim: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. Method: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. Results: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. Conclusion: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

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.