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

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

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

Empirical evidence in early stage software effort estimation using data flow diagram

Software effort estimation in a very early stage of the application development lifecycle is always a challenge for project managers. Many researchers have proposed different methods. They have certain advantages and limitations. This study proposed an approach called Early Effort Estimation from the data flow diagram with a complexity tag added that could estimate the effort estimation in a very early stage of the software development cycle. This method can be obtained by evaluating the customized data flow diagram, a data flow diagram with specific tag values. We also proposed a tool that implemented this method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.IGA/CebiaTech/2021/00

A review of use case-based development effort estimation methods in the system development context

Software Effort Estimation – (further only SEE), is a critical factor in the early phase of the software development life-cycle and hence - the success or failure of a software project depends on the accuracy of the estimated effort. In recent years, Use Cases for Software Effort Estimation has gained wide-ranging popularity. It is suitable for Effort Estimation in the early stages of software development since it helps project managers to bid on projects, and to efficiently allocate resources. It has attracted many researchers’ interest in Use Case-based approaches due to the promising results obtained - including their early applicability. In this article, we look into a systematic review of previously published materials in order to summarise various Software Effort Estimation – (further only SEE), models and developments, based on Use Case Point. The study also provides insights into the effects of all factors that contributed to the Use Case size as an estimation for effort. Apart from this, the paper also provides standard criteria to evaluate the models’ accuracy and effectiveness. © Springer Nature Switzerland AG 2019.Faculty of Applied Informatics, Tomas Bata University in Zlin [RO30196021025, IGA/CebiaTech/2019/002

An approach to adjust effort estimation of function point analysis

This study presents a modified approach to adjust a software development effort estimation. The AdamOptimizer-based regression model is adopted to adjust and enhance the accuracy of effort estimation. This approach is derived into three phases. The first step deals with the logarithmized formula of effort estimation computed by Function Point Analysis and Productivity Delivery Rate. The Adam-Optimizer-based regression model is examined in the second phase, and the ISBSG repository 2020 release R1 is considered as a historical dataset in this paper. Moreover, the K-Fold cross-validation technique is adopted to tunning the training model. In the following phase, all results are evaluated by statistical significance and the goodness of fit measure. Finally, a proposed approach is compared with others: Capers Jones, and the Mean Effort. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.IGA/CebiaTech/2021/00

A productivity optimising model for improving software effort estimation

The estimation of software development effort is a critical task for the effective management of any software industry. Despite the fact that it has been under development for a long time - along with many contributions from many authors seeking to improve the accuracy of software effort estimation, it is still of great interest to many researchers. This study proposed an improved effort estimation model, named the Productivity Optimising Model. This model was designed, based on the Function Points Measurement method and the Multiple Linear Regression model. The Multiple Linear Regression model was built based on the research of historical datasets in order to provide an estimation model so that one can determine the optimising productivity, and then it is easy to calculate the effort. The effort result of this model was compared to the others that were calculated by the Mean Value of Productivity of the tested dataset, and the Capers Jones method. It proved that proposed method gives better accuracy results than the other models. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

Calibrating function complexity in enhancement project for improving function points analysis estimation

Producing a good software product on time and within budget, the initial software estimation takes a significant role. Reality has shown that most software fails because the initial software estimation is not correct. Many researchers have proposed methods for software estimation. It has been developed since the 70s of the last century, but it is still of great interest until now. We also know that creating a new software product is difficult; it is even more difficult to innovate. In the framework of this paper, we propose an improved method based on the FPA method of IFPUG. We named this proposed model is Calibrating Function Complexity in Enhancement Project (CFCEP). This method is based on the Linear Regression technique to give coefficients of function complexity. The experimental results based on the ISBSG dataset show that the estimation based on this new coefficient gives much better results than using the coefficients of the standard FPA method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.IGA/CebiaTech/2021/00