AdamOptimizer for the optimisation of Use Case Points estimation

Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

An evaluation of technical and environmental complexity factors for improving use case points estimation

This paper presents a proposed method for improving the prediction ability of the Use Case Points method. Our main goal is to use the Least Absolute Shrinkage and Selection Operator Regression methods to find out which of the technical and environmental complexity factors significantly affect the accuracy of the Use Case Points method. Two regression models were used to calculate the selected significant variables. The results of several evaluation measures show that the proposed estimation method ability is better than the original Use Case Points method. The Sum of Squared Error of the proposed method is better than the results obtained by the original one. The study also enables project managers to understand how to assess the technical and environmental complexity factors better - since they do have an important impact on effort estimation. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

A new approach to calibrating functional complexity weight in software development effort estimation

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.RVO/FAI/2021/002Faculty of Applied Informatics, Tomas Bata University in Zlin [RVO/FAI/2021/002

Parametric software effort estimation based on optimizing correction factors and multiple linear regression

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. AuthorFaculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2021/001, RO30216002025

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K-nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points-based single methods, optimization correction factors-based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p-value for the t-test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.Faculty of Applied Informatics, Tomas Bata University, (IGA/CebiaTech/2022/001, RVO/FAI/2021/002)Tomas Bata University in Zlin [RVO/FAI/2021/002, IGA/CebiaTech/2022/001

Spatiotemporal evolution of SARS-CoV-2 Alpha and Delta variants during large nationwide outbreak of COVID-19, Vietnam, 2021

We analyzed 1,303 SARS-CoV-2 whole-genome sequences from Vietnam, and found the Alpha and Delta variants were responsible for a large nationwide outbreak of COVID-19 in 2021. The Delta variant was confined to the AY.57 lineage and caused >1.7 million infections and >32,000 deaths. Viral transmission was strongly affected by nonpharmaceutical interventions

Towards a correction factors-based software productivity using ensemble approach for early software development effort estimation

Accuracy of effort estimation is one of the necessary conditions for efficiently managing software development projects. Since the information available in the early stages of software development is insufficient, software sizing metrics are considered critical factors for effort estimation. However, there is no consistent method for converting software sizing into the corresponding effort. Previous estimation methods have not considered software productivity a critical factor in estimating effort based on software sizing. This paper proposes a software productivity model based on correction factors in the Optimizing Correction Factors method through an ensemble construction mechanism of three popular machine learning techniques. The results show that using the proposed software productivity minimizes the estimation error of the methods compared to using fixed productivity metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.IGA/CebiaTech/2022/00

Toward applying agglomerative hierarchical clustering in improving the software development effort estimation

Background: There are many studies on the effect of data clustering on the effort estimation process. Most of them are on partitioning and density-based clustering, and some use hierarchical clustering but fewer details on the linkage methods. Aim: we concentrate on the aspect of the agglomerative hierarchical clustering algorithm’s effectiveness on the accuracy of the effort estimation. Method: We used the agglomerative hierarchical clustering algorithm to group the data into clusters then performed the IFPUG FPA method for effort estimation. The ISBSG dataset was used in this study. The number of clusters is determined using the dendrogram’s cut points. Different cut points and linkage methods were employed to cluster the dataset for the comparison. The estimated results of these clusters were compared with the result from the whole dataset without clustering. Result: with the selected number of clusters, results are consistently better than without clustering with all selected evaluation criteria. Conclusion: the accuracy of the effort estimation can be significantly improved when using agglomerative hierarchical clustering. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

A review of software effort estimation by using functional points analysis

The estimation of the Software Development Effort, (further only SDE), value is critical for the effective management of any software industry. Function Point Analysis, (further only FPA) - is a standardised method designed to systematically measure the functional size of the software. Although this method tool has been become widely-used by many software organisations, it still faces many problems. In this paper, we shall present a systematic review of Software Effort Estimation, (further only SEE), methods based on Functional Points Analysis, (further only FPA). The article focuses on an analysis of the limitations and accuracy of the FPA method - which was proposed many years ago. © 2019, Springer Nature Switzerland AG.Faculty of Applied Informatics, Tomas Bata University in Zlin [RO30196021025, IGA/CebiaTech/2019/002

Analyzing correlation of the relationship between technical complexity factors and environmental complexity factors for software development effort estimation

In this paper, a new method called Correlation-based Feature Selection in Correction Factors is proposed. The method is based on the feature selection method used in software development effort estimation to reduce redundant correction factors. In this paper, the impact of correlation-based feature selection on the method’s estimation accuracy is investigated. Multiple linear regression was used as the basic technique for the correction factors preprocessed by the feature selection method. The results were evaluated using six unbiased accuracy measures through the 5-fold cross-validation over the historical dataset. The proposed method leads to a significant improvement in estimation accuracy by simplifying the evaluation of correction factor values in the use case points method, thus increasing the usefulness of the proposed method in practice. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.IGA/CebiaTech/2021/00