Petr Silhavy

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.

Modern Trends and Techniques in Computer Science

This book is based on the research papers presented in the 3rd Computer Science On-line Conference 2014 (CSOC 2014).The conference is intended to provide an international forum for discussions on the latest high-quality research results in all areas related to Computer Science. The topics addressed are the theoretical aspects and applications of Artificial Intelligences, Computer Science, Informatics and Software Engineering. The authors provide new approaches and methods to real-world problems, and in particular, exploratory research that describes novel approaches in their field. Particular emphasis is laid on modern trends in selected fields of interest. New algorithms or methods in a variety of fields are also presented. This book is divided into three sections and covers topics including Artificial Intelligence, Computer Science and Software Engineering. Each section consists of new theoretical contributions and applications which can be used for the further development of knowledge of everybody who is looking for new knowledge or new inspiration for further research.

Evaluating subset selection methods for use case points estimation

Abstract 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.

Web-based service portal in healthcare

Information delivery is one the most important task in healthcare. The growing sector of electronic healthcare has an important impact on the information delivery. There are two basic approaches towards information delivering. The first is web portal and second is touch-screen terminal. The aim of this paper is to investigate the web-based service portal. The most important advantage of web-based portal in the field of healthcare is an independent access for patients. This paper deals with the conditions and frameworks for healthcare portals

Software Engineering in Intelligent Systems

This volume is based on the research papers presented in the 4th Computer Science On-line Conference. The volume Software Engineering in Intelligent Systems presents new approaches and methods to real-world problems, and in particular, exploratory research that describes novel approaches in the field of Software Engineering. Particular emphasis is laid on modern trends in selected fields of interest. New algorithms or methods in a variety of fields are also presented. The Computer Science On-line Conference (CSOC 2015) is intended to provide an international forum for discussions on the latest high-quality research results in all areas related to Computer Science. The addressed topics are the theoretical aspects and applications of Computer Science, Artificial Intelligences, Cybernetics, Automation Control Theory and Software Engineering.

Patients’ Perspective of the Design of Provider-Patients Electronic Communication Services

Information Delivery is one the most important tasks in healthcare practice. This article discusses patient’s tasks and perspectives, which are then used to design a new Effective Electronic Methodology. The system design methods applicable to electronic communication in the healthcare sector are also described. The architecture and the methodology for the healthcare service portal are set out in the proposed system design.

The Effects of Clustering to Software Size Estimation for the Use Case Points Methods

The main objective of the paper is to present the suitability and effects of several different clustering methods for improving accuracy of software size estimation. For software size estimation was used the Algorithmic Optimisation Method (AOM), which is based Use Case Points (UCP) method. The comparison of K-means, Hierarchical and Density-based clustering is provided. Gap, Silhouette and Calinski-Harabasz criterion were selected as an evaluation criterion for clustering quality. Estimation ability of clustered model is compared on Sum of squared error (SSE). Results shows that clustering improves an estimation ability.

Evaluation of Data Clustering for Stepwise Linear Regression on Use Case Points Estimation

In this paper, stepwise linear regression model in conjunction with clustering for effort estimation is investigated. Effect of clustering is compared to Use Case Points model. The 2 to 20 clusters were tested. As shown increasing a number of clusters brings lower prediction errors. More clusters lower a distance between clusters members, which allows to construct more capable stepwise linear regression model.

Applied Least Square Regression in Use Case Estimation Precision Tuning

In the presented paper the new software effort estimation method is proposed. The Least Square Regression is used to predict a value of correction parameters, which have a significant impact. The accuracy estimationis of 85% better than the convectional use case points methods in tested dataset.

Requirements Based Estimation Approach for System Engineering Projects

In this paper the requirements are used for the purpose of the estimation. Requirements are cluster according to their complexity. The new approach in the papers is based on requirements analysis. The complexity of the requirements is set and Total Requirements Points Value (TRP) is calculated. The Total Requirements Points are modified by the technical and environmental factors, which described the problem domain and the development team experiences. The Total Requirements point can be used as a coefficient for the system size. According this approach system-engineering project can be compared and priced.